[Trash] Automated Inference on Criminality using Face Images
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper trash wrong phrenology physiognomy face facial criminal violent features body physical visible intuition smile micro expression
This paper sets out to build a classifier to distinguish criminals from non-criminals using nothing but a face picture. I explore why the research is trash and what lessons we can learn from it.

https://arxiv.org/abs/1611.04135

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 Hi there, take a look at these faces. Try to decide which of these faces are criminals and which ones are law-abiding citizens. I'll give you a second. Okay, got it. So if you decided that these four here are the criminals, you would be correct. And that makes these three the law-abiding citizens. As for this one, maybe if the crime is being too cool. Of course, none of these faces actually exist in real life. These are compositions of eigenfaces, of datasets, of criminals and non-criminals. Today's paper is an absolute controversy. This is going to get me into so much trouble. So if you see something like this in the news, always, always, always go and check. Now we're going to look at automated inference on criminality using face images by Xiaolin Wu and Xi Cheng. On a high level, they're trying to separate criminals from non-criminals using face images. So basically using classifiers on ID photos. This, of course, has generated quite the uproar. I suggest we just dive into the paper and look at what's happening right here. We study for the first time automated inference on criminality based solely on still face images, which is free of any biases and subjective judgments of human observers. So they say we train a bunch of models, including, as you can see, a CNN, using facial images of one thousand eight hundred and fifty six real persons controlled for race, gender, age and facial expressions. Nearly half of whom were convicted criminals for discriminating between criminals and non-criminals. So this is the outset. This is the kind of research question here. Now, immediately you have people jumping up saying that's not possible. And I would agree. But I think actually there are very, very interesting lessons to be learned from this paper. So they're saying they actually managed to do this with their classifiers, actually with all of these classifiers. Of course, deep learning being the best. Also, some discriminating structural features for predicting criminality have been found by machine learning. So they even tell you why. Above all, the most important discovery of this research is that criminal and non-criminal face images populate two quite distinctive manifolds. The variation among criminal faces is significantly greater than that of non-criminal faces. The two manifolds consisting of criminal and non-criminal faces appear to be concentric with the non-criminal manifold lying in the kernel with the smaller span exhibiting a law of normality for faces of non-criminals. Oh, I'm going to be canceled. I don't advocate for this. This is not, this is not, I'm not a fan of this. Just in other words, the faces of general law abiding public have a greater degree of resemblance compared with the faces of criminals. Or criminals have a higher degree of dissimilarity in facial appearance than non-criminals. So basically what they're saying is that the this kind of similarity among the non-criminals in their data set is larger than the similarity among the criminals. OK, so already the outset, right? Then they go into this introduction and in the introduction we won't go through it fully, but they basically introduce the concept of facial recognition. They try to build up kind of an argument where they say faces are different. Some people have hypothesized that it's possible to infer personality traits from facial features. Some studies exist that show that people agree on the perception of these traits. So not the actual traits, but people will kind of agree that a face looks extroverted or more agreeable. People tend to agree that the appearance exists. And then they sort of make the next step and say, OK, can facial features also be used not just for predicting the appearance, but to predict the actual personality trait? For validating the hypothesis on the correlations between the innate traits and social behaviors of a person and the physical characteristics of that person's face, it would be hard pushed to find a more convincing experiment than examining the success rate of discriminating between criminals and non-criminals. So actually, you could agree with this, right? Since this is sort of a distinction one can make about behavior, whether or not someone breaks the law or in this case is caught and convicted and so on. There are like many, many hurdles in this. In essence, the statement sort of makes sense. Like if you could actually do this from facial features, that would be very, first of all, very surprising and second of all, very drastic. People immediately jump to the conclusion that, OK, if such a thing were found, that means you could somehow precognate criminality, which I don't think it has to be, because what could also be the case is they have a quote from Aristotle right here. It is possible to infer character from features if it is granted that body and soul are changed together by the natural affections. One interpretation of me is that, let's say you break the law for whatever, it could be completely moral, like you steal the medicine from the old lady in your house and but you know you broke the law, you know you did something that society doesn't want you to do and that will exert stress on you. You now have to lie to people about this. You now have to sort of make sure you're not caught. You have to worry. Maybe there's a security tape or something like this. And the stress will, we know that stress will physically change you. And that could be in turn made out by your features. For example, the stress of being in jail could change your physical features. And since these are all convicted criminals, one might think that it might be possible. It might. Again, not saying it is, it might. So if we throw away all of the kind of prejudgments on this, it could be an interesting research question, right? Could. Now, whether we want to pursue it or not, that's a different question. But the way they build this up here is that they only have the best of intentions in mind. I feel like this might not be the case. So they say something like this right here. At the onset of this study, our gut feeling is that modern tools of machine learning and computer vision will refute the validity of physiognomy, although the outcomes turn out otherwise. This and this is the part where I just stopped believing them that their intentions were like all good. And it's just about disproving this so we can just lay it to rest because they then very quickly switch when they find something else. Non criminals are the normals and the criminals are like the that just rubs me the wrong way where you'll have to say no. It's like the Pekuk looks like, oh, no, we, you know, we have many social gatherings and our gut feeling is that people aren't really different. And the robes are actually personal protective equipment. It's all actually just a community thing. We all have, you know, good intentions. Oh, and every now and then we lynch you guys going into this with sort of a mixed bag of feelings where you'd have a hypothetically valid research question. But also even the introduction makes it very clear because it's somewhat over the top promising to just be neutral and be good, good intended. Not going to fall for it. Sorry. They say in order to conduct our experiments, they have one thousand eight hundred and fifty six ID photos. The following criteria, Chinese male between ages of 18 and 55, no facial hair, no facial scars or other markings. And the data set is called S. Then there's two subsets, S.N. for non criminals and S.C. for criminals. The non criminals contains ID photos of alone hundred twenty six non criminals that were acquired from the Internet using the web spider tool. They're from a wide gamut of professions and social status, including waiters, construction work, blah, blah, blah. OK. The subset of the criminals contains ID photos of seven hundred and thirty criminals of which three hundred and thirty as are published as wanted suspects by the Ministry of Public Security of China and by the Departments of Public Security for the provinces of Guangdong, Jiangsu, Liaoning, et cetera. The others are provided by city police department in China under a confidentiality agreement. We stress and here here's an important point. We stress that the criminal face images in S.C. are normal ID photos, not police mugshots. So they say they have violent crimes or nonviolent crimes. And so on. So they have these examples here of those images. So the top ones are the criminals and the bottom ones are the non criminals. Now, people immediately see differences here. And if you spotted that all of these have white colors and none of those have white colors, then you would be correct. Now, you're on the right path. You're not actually correct. Correct. But you're on the right path here because actually what they do is they they mask away the colors. So they only extract the face part and the upper neck part. So these this white collar part will actually not be on the image that they analyze to control for clothing, which is good. But it gives you sort of an indication that the origins of the two image groups might not actually be the same. So what you'll have is you'll have basically a database, actually have two databases of criminals, which are so the one database is this wanted. Let's call them W. These are released by the police for wanted criminals. Then the others database is the convicted criminals. Let's call that C. And then on the other side, you have the database of non criminals and the non criminals come from the Internet. So you have three different databases. And of course, these two make will going to make up the criminals and this will make up the non criminals. And the herein lies the problem, right? You even though the white collars are masked out, you have to make sure that whatever you find isn't just a property of how you collected the data. And this doesn't really come through in this paper. So they they do data preparation as again, they mask, they resize and so on. They stress again, all our idea images with frontal lighting. So, yeah. And they OK, so now they test the classifiers. So they say we test logistic regression, logistic regression, KNN, SVM and CNN on the image data set. So for the CNN, you can just input the original image. But for the other classifiers, you need a set of features. And what they do is they concatenate three different image feature vectors. So the first one is facial landmark points that you extract by some sort of tool. You can extract whatever corners of mouth and so on. Then the second facial feature vector generated by a modular PCA. And the third is a facial feature vector based on local binary pattern histograms. So these are these are sort of face features that people use for recognizing faces. They concatenate them. That gives you a feature vector. You feed that into the machine learning algorithm. And they do a we perform a tenfold cross validation for all possible combinations of three feature classifiers and the four types of feature vectors plus the data driven CNN. So they do a tenfold cross validation, right, which basically means you do you partition your data into 10 parts. You take nine to train, predict the one. Then you take the next nine to train, predict the one that you left out and so on. But this kind of you get a train test split across all sorts of splits of your data, which is a it's a you know, it's a valid thing to do. And they discover here that their CNN classifier performs at almost 90 percent accuracy, as you can see here. And even their SVM and the other classifiers, they perform fairly well in recognizing these criminality faces. So. And they analyze the ROC curves and the ROC curves. This is a really this is a classifier that works right. So you can see in the the the other models, but especially the CNN classifier here, works really well. Of course, the question is, what does it work for? So they basically say, all right, we now have a classifier that distinguishes criminals from non-criminals. And I would say you have a classifier that discriminates your particular pictures of criminals from your particular pictures of non-criminals. And if this were submitted to me as a reviewer, I would expect that any sane author would then go and try to invalidate that. So here's what you'll have to do if you want to convince me that this is not just due to how you collected your data. You need to go and you need to basically say, OK, I have these different methods of collecting data right here. Now, maybe I can go to the police and ask them for a picture from the same database of a non convicted, not of a non-criminal, someone that was arrested, but then not convicted. And I can have someone from from here. That can put in that data set, and then you have to show me that your classifier will correctly predict that that's a non-criminal. And if it predicts it's a criminal, it's due to the data set. You can also find one of the criminals, but find their picture on the Internet, like you collected the non-criminals. And that will give you someone from this database in that data set. And then you have to show me that your classifier correctly predicts that's a criminal. You can further convince me that your classifier is neutral to this separation right here of the wanted and convicted criminals, because they all should be criminals. So if your classifier is neutral to that, then it basically doesn't care where it comes from. So this will be a weaker argument, but still one that one could investigate. What do they do for validating their method? Here is where it gets funky. So they say, given the high social sensitivities and repercussions of our topic and skeptics on physiognomy, we try to exercise maximum caution before publishing our results. Yeah, you failed. In playing devil's advocate, we design and conduct the following experiments to challenge the validity of the tested classifiers. For the task of discriminating between criminals and non-criminals. All right, this is it right here. Here is where you give us where you tell us it's not because of how we collected the data. Which is the obvious explanation. We randomly label the faces in the very sample set as negative and positive instances with equal probability and redo all the above experiments of binary classification. Well, how crazy is this? All right, they're basically saying, well, if our classifier were not a criminality classifier, that means we could invalidate it by shuffling the labels. And if that comes out to 50-50, then our classifier obviously works because it's not 50-50 in this data set. So basically, they're just validating that a classification algorithm can classify something. The criticism here is never that they haven't actually trained a working classifier. The criticism is what have they trained a classifier for? But their entire validation procedure is basically, we don't have a bug in our code. The outcomes show that the randomly generated negative and positive instances cannot be distinguished at all. Gee, who guessed? A classifier on random labels doesn't generalize. Man, they say, in fact, we go much further along the self-critical path. All right, here it comes. And carry out the same experiments for random labeling on different samples of the same size and with the same variable control. Only this time in the selection criteria are standard ID photos of Chinese female young middle-aged, or standard ID photos of Caucasian male young middle-aged, of Caucasian female young middle-aged nofacial. So basically, if you train on a randomly labeled data set on any sort of pictures, your classifier will not work. Thanks. Thanks. Maybe that's, I think that's the academically most valid statement in the entire paper. Oh, man, in none of the three cases, any of the four classifiers managed to achieve a true positive rate higher than 53% on randomly labeled positive and negative instances. So the classifier must be valid because... OK, the above experiments rule out that the good accuracies of the four evaluated classifiers in phase inference on criminality are due to data overfitting. No. Otherwise, given the same sample size, they would also be able to distinguish between randomly labeled positive and negative instances with significantly better chances. But they did cross validation. They did. The cross validation prevents the overfitting. We no one criticizes that you over. These people have no idea what they're doing. They have no clue of machine learning. They don't know what the problems with methods are. They don't know what overfitting is and and how you control for it. The big jump of the true positive rate from random labeling to truth labeling on the same set of phase images can only be explained by intrinsic separability of SC and SN. That is true. That is true. But why are they separable? That's the question. OK, as different source cameras generated the ID photos in the set S, now they might be on the right track here. Different source cameras. Maybe they get the idea that different data sources lead to different things. They might leave their signatures that, although below perception threshold in signal strength, could mislead machine learning. OK, so they basically saying different cameras could generate different sort of artifacts. And they rule this out by basically adding noise to the images such that these artifacts would be washed out and the noise doesn't change their results. Gee, they were so close. They were so close to actually doing something useful. OK, this section is where it gets even more interesting. Now they're trying to guess from their classifier what are the actual features that make criminals criminals. So discriminating features right here. Having obtained the above strong empirical evidences for the validity of automated phase induced inference on criminality, one cannot resist the following intriguing questions. What features of a human face betray its owners propensity for crimes? OK, Shakespeare. And they basically they basically go and explain ability route where they see what the classifier pays attention to. And it turns out the classifier pays attention to the following features on the left. You can see where the classifier pays attention to. No surprise here, it pays attention to face features, but they kind of parse out the following three features. First of all, the D, the distance between the eyes in criminals tends to be smaller than in non criminals. The angle between the nose and the corners of the mouth tends to be smaller in criminals than in non criminals. And the curvature of the upper lip tends to be higher in criminals than in non criminals. So let's let's try just from this information to draw the ultimate criminal and non criminal faces. So first of all, the non criminal, let's draw the non criminal as just regular. I'm not very good at this. So here's the nose. And then let's just draw the lips like this. Non criminal. Perfect. Looks like a law abiding citizen to me. Criminal. Right here. So the eyes are closer together. Here's the nose. And then the curvature of the upper lip is higher. So. Hmm. And then the angle between the nose and the outer corners of the mouth is smaller. How can I make the angle smaller? Could it be that if I. Oh, yes. Ah, that's the trick. Criminal, ladies and gentlemen. So are you telling me that all someone has to do to be a criminal is frown? Yeah, totally valid. So they're so close, right? But they say, oh, these are intrinsic facial features. But come on. All right. So they go on to say that they have some histogram differences of these features. So they basically say these features are what's responsible for this. And then they do face clustering, which is beautiful. So first of all, what they do is they sort of take the average faces for criminals and non-criminals. And these are the average faces. So the top are the actual average eigen faces. And the bottom is when you kind of shift the facial landmarks around. The seeming paradox that SC and SN can be classified, but the average faces appear almost the same can. Sorry, average faces appear almost the same. The average faces appear almost the same. What a paradox. These are almost the same. I mean, if I just overlay them one over another, they're almost the same. There is no difference at all. I don't see a difference. What could possibly be the difference? What could be the difference? I don't think these are the most honest of intentions. So they basically do some clustering, which I find interesting. I find interesting, for example, that they don't really explain isomap here. So isomap uses the geodesic distance between two points on the manifold, which is defined between the sum of the weights. So they kind of washy washy isomap. But they then explain k-means in great detail with formulas. And again, I mean, OK, non-machine learning people can do machine learning. That's fine. But they're not really into the matter here. And they try k-means clustering. And they find, in their opinion, they find four clusters of criminals and three clusters of non-criminals. Now, why three and four? And usually, you can do something like this by clustering and then measuring the residual variance in your data. So how much does one cluster explain, two clusters, and so on? So here you can see the curves for non-criminals and criminals. Now, they claim that the optimal number of clusters here for non-criminals is three, which makes no sense to me. Like, why three? What you usually want to find is kind of a kink in your curve. Like, if it's steep and then it gets flat, that means that up until then, your clusters actually buy you something good. And from then, they basically are useless. So if I were to guess, I would divide the criminals into two clusters and the non-criminals into a single cluster, because that's pretty flat. Certainly not the non-criminals into three and the criminals into four. That makes no sense at all. Like, why? And they say, OK, these are the clusters right here. And these are the pictures I showed you at the beginning. What surprise? The bottom ones, the non-criminals, are smiling and the top ones aren't. Gee, I wonder why the method works. And the interesting part here is that how can we justify? How can we say if we decide on one cluster for non-criminals and two clusters for criminals, what does that remind us of? Oh, yes, that is exactly how we collected the data. That is exactly the fact that we collected the non-criminals with one procedure and the criminals with two different procedures. Gee, their data set replicates exactly how they collected the data. And that convinces me that it says absolutely nothing about the actual criminality of people. It's just that police, even if it's ID photos, they don't smile. And pictures on the internet, sometimes people smile. The rest of the paper is pretty much garbage. They did reply to critics and they kind of take issue with a number of things. So first, name calling. I don't mean to name call, but it's going to happen. I don't get why people call them racist because it's all the same. Doesn't, no, no, trouble. And smiley. Ha. In our experiments, we did control facial expression, but not faint micro expression. The critique that our methods can be reduced to a simple discriminator of smiling versus not smiling has given us a new angle of scrutiny. They say, well, Westerners think that this is smiling, but our Chinese students and colleagues, even after being prompted to consider the cue of smile, fail to detect the same. So basically, their answer is, yeah, you think so, but we don't. And then they say instead, they only find the faces in the bottom row appearing somewhat more relaxed than those in the top row. And then here's the crucial part. All criminal ID photos are government issues, but not mock shots. They are normal government issue ID portraits like those driver license in the USA. In contrast, most of the non-criminal ID style photos are taken officially by some organizations, such as real estate companies, law firms, et cetera, for their website. You know what it always says when you take your picture for a government ID? Please don't smile. Imagine if your law firm comes to you and say, we want a picture for our website. Please don't smile. All right, this was it for this paper. If you like this content, please consider subscribing and sharing it out. This is absolute garbage. And there is important lessons to learn here, namely Occam's razor is a real problem in research. People often fail to criticize themselves enough and to think, is there maybe a different explanation for why I'm getting the results that I'm getting? And how can I disprove that that is the case? And how can I make sure that the effect that I'm seeing actually comes from the place where I claim it comes from? I think this is a real threat throughout all of research. I've seen many papers that I've reviewed that are exactly of the same fallacy, not as touchy subjects as this one, but it definitely exists. And I remind everyone that learn a lesson from this. And have a good day. Thank you.
 Hi there, take a look at these faces. Try to decide which of these faces are criminals and which ones are law-abiding citizens.
 I'll give you a second.
 Okay, got it. So if you decided that these four here are the criminals, you would be correct.
 And that makes these three the law-abiding citizens.
 As for this one, maybe if the crime is being too cool.
 Of course, none of these faces actually exist in real life.
 These are compositions of eigenfaces, of datasets, of criminals and non-criminals.
 Today's paper is an absolute controversy. This is going to get me into so much trouble.
 So if you see something like this in the news, always, always, always go and check.
 Now we're going to look at automated inference on criminality using face images by Xiaolin Wu and Xi Cheng.
 On a high level, they're trying to separate criminals from non-criminals using face images.
 So basically using classifiers on ID photos.
 This, of course, has generated quite the uproar.
 I suggest we just dive into the paper and look at what's happening right here.
 We study for the first time automated inference on criminality based solely on still face images,
 which is free of any biases and subjective judgments of human observers.
 So they say we train a bunch of models, including, as you can see, a CNN,
 using facial images of one thousand eight hundred and fifty six real persons controlled for race, gender, age and facial expressions.
 Nearly half of whom were convicted criminals for discriminating between criminals and non-criminals.
 So this is the outset. This is the kind of research question here.
 Now, immediately you have people jumping up saying that's not possible.
 And I would agree.
 But I think actually there are very, very interesting lessons to be learned from this paper.
 So they're saying they actually managed to do this with their classifiers, actually with all of these classifiers.
 Of course, deep learning being the best.
 Also, some discriminating structural features for predicting criminality have been found by machine learning.
 So they even tell you why.
 Above all, the most important discovery of this research is that criminal and non-criminal face images populate two quite distinctive manifolds.
 The variation among criminal faces is significantly greater than that of non-criminal faces.
 The two manifolds consisting of criminal and non-criminal faces appear to be concentric with the non-criminal manifold lying in the kernel
 with the smaller span exhibiting a law of normality for faces of non-criminals.
 Oh, I'm going to be canceled.
 I don't advocate for this.
 This is not, this is not, I'm not a fan of this.
 Just in other words, the faces of general law abiding public have a greater degree of resemblance compared with the faces of criminals.
 Or criminals have a higher degree of dissimilarity in facial appearance than non-criminals.
 So basically what they're saying is that the this kind of similarity among the non-criminals in their data set is larger than the similarity among the criminals.
 OK, so already the outset, right?
 Then they go into this introduction and in the introduction we won't go through it fully, but they basically introduce the concept of facial recognition.
 They try to build up kind of an argument where they say faces are different.
 Some people have hypothesized that it's possible to infer personality traits from facial features.
 Some studies exist that show that people agree on the perception of these traits.
 So not the actual traits, but people will kind of agree that a face looks extroverted or more agreeable.
 People tend to agree that the appearance exists.
 And then they sort of make the next step and say, OK, can facial features also be used not just for predicting the appearance, but to predict the actual personality trait?
 For validating the hypothesis on the correlations between the innate traits and social behaviors of a person and the physical characteristics of that person's face,
 it would be hard pushed to find a more convincing experiment than examining the success rate of discriminating between criminals and non-criminals.
 So actually, you could agree with this, right?
 Since this is sort of a distinction one can make about behavior, whether or not someone breaks the law or in this case is caught and convicted and so on.
 There are like many, many hurdles in this.
 In essence, the statement sort of makes sense.
 Like if you could actually do this from facial features, that would be very, first of all, very surprising and second of all, very drastic.
 People immediately jump to the conclusion that, OK, if such a thing were found, that means you could somehow precognate criminality,
 which I don't think it has to be, because what could also be the case is they have a quote from Aristotle right here.
 It is possible to infer character from features if it is granted that body and soul are changed together by the natural affections.
 One interpretation of me is that, let's say you break the law for whatever, it could be completely moral,
 like you steal the medicine from the old lady in your house and but you know you broke the law,
 you know you did something that society doesn't want you to do and that will exert stress on you.
 You now have to lie to people about this.
 You now have to sort of make sure you're not caught.
 You have to worry. Maybe there's a security tape or something like this.
 And the stress will, we know that stress will physically change you.
 And that could be in turn made out by your features.
 For example, the stress of being in jail could change your physical features.
 And since these are all convicted criminals, one might think that it might be possible.
 It might. Again, not saying it is, it might.
 So if we throw away all of the kind of prejudgments on this, it could be an interesting research question, right?
 Could. Now, whether we want to pursue it or not, that's a different question.
 But the way they build this up here is that they only have the best of intentions in mind.
 I feel like this might not be the case.
 So they say something like this right here.
 At the onset of this study, our gut feeling is that modern tools of machine learning and computer vision will refute the validity of physiognomy,
 although the outcomes turn out otherwise.
 This and this is the part where I just stopped believing them that their intentions were like all good.
 And it's just about disproving this so we can just lay it to rest because they then very quickly switch when they find something else.
 Non criminals are the normals and the criminals are like the that just rubs me the wrong way where you'll have to say no.
 It's like the Pekuk looks like, oh, no, we, you know, we have many social gatherings and our gut feeling is that people aren't really different.
 And the robes are actually personal protective equipment.
 It's all actually just a community thing.
 We all have, you know, good intentions.
 Oh, and every now and then we lynch you guys going into this with sort of a mixed bag of feelings where you'd have a hypothetically valid research question.
 But also even the introduction makes it very clear because it's somewhat over the top promising to just be neutral and be good, good intended.
 Not going to fall for it. Sorry.
 They say in order to conduct our experiments, they have one thousand eight hundred and fifty six ID photos.
 The following criteria, Chinese male between ages of 18 and 55, no facial hair, no facial scars or other markings.
 And the data set is called S.
 Then there's two subsets, S.N. for non criminals and S.C. for criminals.
 The non criminals contains ID photos of alone hundred twenty six non criminals that were acquired from the Internet using the web spider tool.
 They're from a wide gamut of professions and social status, including waiters, construction work, blah, blah, blah.
 OK. The subset of the criminals contains ID photos of seven hundred and thirty criminals of which three hundred and thirty as are published as wanted suspects by the Ministry of Public Security of China
 and by the Departments of Public Security for the provinces of Guangdong, Jiangsu, Liaoning, et cetera.
 The others are provided by city police department in China under a confidentiality agreement.
 We stress and here here's an important point.
 We stress that the criminal face images in S.C. are normal ID photos, not police mugshots.
 So they say they have violent crimes or nonviolent crimes.
 And so on. So they have these examples here of those images.
 So the top ones are the criminals and the bottom ones are the non criminals.
 Now, people immediately see differences here.
 And if you spotted that all of these have white colors and none of those have white colors, then you would be correct.
 Now, you're on the right path. You're not actually correct.
 Correct. But you're on the right path here because actually what they do is they they mask away the colors.
 So they only extract the face part and the upper neck part.
 So these this white collar part will actually not be on the image that they analyze to control for clothing, which is good.
 But it gives you sort of an indication that the origins of the two image groups might not actually be the same.
 So what you'll have is you'll have basically a database, actually have two databases of criminals, which are so the one database is this wanted.
 Let's call them W. These are released by the police for wanted criminals.
 Then the others database is the convicted criminals.
 Let's call that C. And then on the other side, you have the database of non criminals and the non criminals come from the Internet.
 So you have three different databases.
 And of course, these two make will going to make up the criminals and this will make up the non criminals.
 And the herein lies the problem, right?
 You even though the white collars are masked out, you have to make sure that whatever you find isn't just a property of how you collected the data.
 And this doesn't really come through in this paper.
 So they they do data preparation as again, they mask, they resize and so on.
 They stress again, all our idea images with frontal lighting.
 So, yeah. And they OK, so now they test the classifiers.
 So they say we test logistic regression, logistic regression, KNN, SVM and CNN on the image data set.
 So for the CNN, you can just input the original image.
 But for the other classifiers, you need a set of features.
 And what they do is they concatenate three different image feature vectors.
 So the first one is facial landmark points that you extract by some sort of tool.
 You can extract whatever corners of mouth and so on.
 Then the second facial feature vector generated by a modular PCA. And the third is a facial feature vector based on local binary pattern histograms.
 So these are these are sort of face features that people use for recognizing faces.
 They concatenate them. That gives you a feature vector. You feed that into the machine learning algorithm.
 And they do a we perform a tenfold cross validation for all possible combinations of three feature classifiers and the four types of feature vectors plus the data driven CNN.
 So they do a tenfold cross validation, right, which basically means you do you partition your data into 10 parts.
 You take nine to train, predict the one.
 Then you take the next nine to train, predict the one that you left out and so on.
 But this kind of you get a train test split across all sorts of splits of your data, which is a it's a you know, it's a valid thing to do.
 And they discover here that their CNN classifier performs at almost 90 percent accuracy, as you can see here.
 And even their SVM and the other classifiers, they perform fairly well in recognizing these criminality faces.
 So. And they analyze the ROC curves and the ROC curves.
 This is a really this is a classifier that works right.
 So you can see in the the the other models, but especially the CNN classifier here, works really well.
 Of course, the question is, what does it work for?
 So they basically say, all right, we now have a classifier that distinguishes criminals from non-criminals.
 And I would say you have a classifier that discriminates your particular pictures of criminals from your particular pictures of non-criminals.
 And if this were submitted to me as a reviewer, I would expect that any sane author would then go and try to invalidate that.
 So here's what you'll have to do if you want to convince me that this is not just due to how you collected your data.
 You need to go and you need to basically say, OK, I have these different methods of collecting data right here.
 Now, maybe I can go to the police and ask them for a picture from the same database of a non convicted,
 not of a non-criminal, someone that was arrested, but then not convicted.
 And I can have someone from from here.
 That can put in that data set, and then you have to show me that your classifier will correctly predict that that's a non-criminal.
 And if it predicts it's a criminal, it's due to the data set.
 You can also find one of the criminals, but find their picture on the Internet, like you collected the non-criminals.
 And that will give you someone from this database in that data set.
 And then you have to show me that your classifier correctly predicts that's a criminal.
 You can further convince me that your classifier is neutral to this separation right here of the wanted and convicted criminals,
 because they all should be criminals.
 So if your classifier is neutral to that, then it basically doesn't care where it comes from.
 So this will be a weaker argument, but still one that one could investigate.
 What do they do for validating their method?
 Here is where it gets funky.
 So they say, given the high social sensitivities and repercussions of our topic and skeptics on physiognomy,
 we try to exercise maximum caution before publishing our results.
 Yeah, you failed.
 In playing devil's advocate, we design and conduct the following experiments to challenge the validity of the tested classifiers.
 For the task of discriminating between criminals and non-criminals.
 All right, this is it right here.
 Here is where you give us where you tell us it's not because of how we collected the data.
 Which is the obvious explanation.
 We randomly label the faces in the very sample set as negative and positive instances with equal probability
 and redo all the above experiments of binary classification.
 Well, how crazy is this?
 All right, they're basically saying, well, if our classifier were not a criminality classifier,
 that means we could invalidate it by shuffling the labels.
 And if that comes out to 50-50, then our classifier obviously works because it's not 50-50 in this data set.
 So basically, they're just validating that a classification algorithm can classify something.
 The criticism here is never that they haven't actually trained a working classifier.
 The criticism is what have they trained a classifier for?
 But their entire validation procedure is basically, we don't have a bug in our code.
 The outcomes show that the randomly generated negative and positive instances cannot be distinguished at all.
 Gee, who guessed?
 A classifier on random labels doesn't generalize.
 Man, they say, in fact, we go much further along the self-critical path.
 All right, here it comes.
 And carry out the same experiments for random labeling on different samples of the same size and with the same variable control.
 Only this time in the selection criteria are standard ID photos of Chinese female young middle-aged,
 or standard ID photos of Caucasian male young middle-aged, of Caucasian female young middle-aged nofacial.
 So basically, if you train on a randomly labeled data set on any sort of pictures, your classifier will not work.
 Thanks. Thanks.
 Maybe that's, I think that's the academically most valid statement in the entire paper.
 Oh, man, in none of the three cases, any of the four classifiers managed to achieve a true positive rate higher than 53% on randomly labeled positive and negative instances.
 So the classifier must be valid because...
 OK, the above experiments rule out that the good accuracies of the four evaluated classifiers in phase inference on criminality are due to data overfitting.
 No.
 Otherwise, given the same sample size, they would also be able to distinguish between randomly labeled positive and negative instances with significantly better chances.
 But they did cross validation.
 They did. The cross validation prevents the overfitting.
 We no one criticizes that you over.
 These people have no idea what they're doing.
 They have no clue of machine learning.
 They don't know what the problems with methods are.
 They don't know what overfitting is and and how you control for it.
 The big jump of the true positive rate from random labeling to truth labeling on the same set of phase images can only be explained by intrinsic separability of SC and SN.
 That is true. That is true.
 But why are they separable?
 That's the question.
 OK, as different source cameras generated the ID photos in the set S, now they might be on the right track here.
 Different source cameras.
 Maybe they get the idea that different data sources lead to different things.
 They might leave their signatures that, although below perception threshold in signal strength, could mislead machine learning.
 OK, so they basically saying different cameras could generate different sort of artifacts.
 And they rule this out by basically adding noise to the images such that these artifacts would be washed out and the noise doesn't change their results.
 Gee, they were so close.
 They were so close to actually doing something useful.
 OK, this section is where it gets even more interesting.
 Now they're trying to guess from their classifier what are the actual features that make criminals criminals.
 So discriminating features right here.
 Having obtained the above strong empirical evidences for the validity of automated phase induced inference on criminality,
 one cannot resist the following intriguing questions.
 What features of a human face betray its owners propensity for crimes?
 OK, Shakespeare.
 And they basically they basically go and explain ability route where they see what the classifier pays attention to.
 And it turns out the classifier pays attention to the following features on the left.
 You can see where the classifier pays attention to.
 No surprise here, it pays attention to face features, but they kind of parse out the following three features.
 First of all, the D, the distance between the eyes in criminals tends to be smaller than in non criminals.
 The angle between the nose and the corners of the mouth tends to be smaller in criminals than in non criminals.
 And the curvature of the upper lip tends to be higher in criminals than in non criminals.
 So let's let's try just from this information to draw the ultimate criminal and non criminal faces.
 So first of all, the non criminal, let's draw the non criminal as just regular.
 I'm not very good at this.
 So here's the nose.
 And then let's just draw the lips like this.
 Non criminal.
 Perfect.
 Looks like a law abiding citizen to me.
 Criminal.
 Right here.
 So the eyes are closer together.
 Here's the nose.
 And then the curvature of the upper lip is higher.
 So.
 Hmm.
 And then the angle between the nose and the outer corners of the mouth is smaller.
 How can I make the angle smaller?
 Could it be that if I.
 Oh, yes.
 Ah, that's the trick.
 Criminal, ladies and gentlemen.
 So are you telling me that all someone has to do to be a criminal is frown?
 Yeah, totally valid.
 So they're so close, right?
 But they say, oh, these are intrinsic facial features.
 But come on.
 All right.
 So they go on to say that they have some histogram differences of these features.
 So they basically say these features are what's responsible for this.
 And then they do face clustering, which is beautiful.
 So first of all, what they do is they sort of take the average faces
 for criminals and non-criminals.
 And these are the average faces.
 So the top are the actual average eigen faces.
 And the bottom is when you kind of shift the facial landmarks around.
 The seeming paradox that SC and SN can be classified,
 but the average faces appear almost the same can.
 Sorry, average faces appear almost the same.
 The average faces appear almost the same.
 What a paradox.
 These are almost the same.
 I mean, if I just overlay them one over another, they're almost the same.
 There is no difference at all.
 I don't see a difference.
 What could possibly be the difference?
 What could be the difference?
 I don't think these are the most honest of intentions.
 So they basically do some clustering, which I find interesting.
 I find interesting, for example, that they don't really explain isomap here.
 So isomap uses the geodesic distance between two points on the manifold,
 which is defined between the sum of the weights.
 So they kind of washy washy isomap.
 But they then explain k-means in great detail with formulas.
 And again, I mean, OK, non-machine learning people
 can do machine learning.
 That's fine.
 But they're not really into the matter here.
 And they try k-means clustering.
 And they find, in their opinion, they find four clusters of criminals
 and three clusters of non-criminals.
 Now, why three and four?
 And usually, you can do something like this by clustering
 and then measuring the residual variance in your data.
 So how much does one cluster explain, two clusters, and so on?
 So here you can see the curves for non-criminals and criminals.
 Now, they claim that the optimal number of clusters here for non-criminals
 is three, which makes no sense to me.
 Like, why three?
 What you usually want to find is kind of a kink in your curve.
 Like, if it's steep and then it gets flat,
 that means that up until then, your clusters actually buy you something good.
 And from then, they basically are useless.
 So if I were to guess, I would divide the criminals into two clusters
 and the non-criminals into a single cluster, because that's pretty flat.
 Certainly not the non-criminals into three and the criminals into four.
 That makes no sense at all.
 Like, why?
 And they say, OK, these are the clusters right here.
 And these are the pictures I showed you at the beginning.
 What surprise?
 The bottom ones, the non-criminals, are smiling and the top ones aren't.
 Gee, I wonder why the method works.
 And the interesting part here is that how can we justify?
 How can we say if we decide on one cluster for non-criminals
 and two clusters for criminals, what does
 that remind us of?
 Oh, yes, that is exactly how we collected the data.
 That is exactly the fact that we collected the non-criminals
 with one procedure and the criminals with two different procedures.
 Gee, their data set replicates exactly how they collected the data.
 And that convinces me that it says absolutely nothing
 about the actual criminality of people.
 It's just that police, even if it's ID photos, they don't smile.
 And pictures on the internet, sometimes people smile.
 The rest of the paper is pretty much garbage.
 They did reply to critics and they kind of take issue with a number of things.
 So first, name calling.
 I don't mean to name call, but it's going to happen.
 I don't get why people call them racist because it's all the same.
 Doesn't, no, no, trouble.
 And smiley.
 Ha.
 In our experiments, we did control facial expression,
 but not faint micro expression.
 The critique that our methods can be reduced
 to a simple discriminator of smiling versus not smiling
 has given us a new angle of scrutiny.
 They say, well, Westerners think that this is smiling,
 but our Chinese students and colleagues, even after being prompted
 to consider the cue of smile, fail to detect the same.
 So basically, their answer is, yeah, you think so, but we don't.
 And then they say instead, they only find the faces in the bottom row
 appearing somewhat more relaxed than those in the top row.
 And then here's the crucial part.
 All criminal ID photos are government issues, but not mock shots.
 They are normal government issue ID portraits
 like those driver license in the USA.
 In contrast, most of the non-criminal ID style photos
 are taken officially by some organizations,
 such as real estate companies, law firms, et cetera, for their website.
 You know what it always says when you take your picture for a government ID?
 Please don't smile.
 Imagine if your law firm comes to you and say,
 we want a picture for our website.
 Please don't smile.
 All right, this was it for this paper.
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 This is absolute garbage.
 And there is important lessons to learn here,
 namely Occam's razor is a real problem in research.
 People often fail to criticize themselves enough
 and to think, is there maybe a different explanation for why
 I'm getting the results that I'm getting?
 And how can I disprove that that is the case?
 And how can I make sure that the effect that I'm seeing actually
 comes from the place where I claim it comes from?
 I think this is a real threat throughout all of research.
 I've seen many papers that I've reviewed that
 are exactly of the same fallacy, not as touchy subjects as this one,
 but it definitely exists.
 And I remind everyone that learn a lesson from this.
 And have a good day.
 Thank you.