Facial expression recognition   HMM
224867919
Gabor Filter and ICA Based Facial Expression Recognition Using Two Layered Hidden Markov Model
This paper introduces the framework based on the extraction of features using Gabor filters and modified hidden Markov model for classification. The three regions of interest (nose, mouth and eyes) are extracted using Gabor filters and dimensions are reduced by independent component analysis. Then these reduced dimensions are input to our two layered HMM for training and testing. Seven facial expressions are recognized using publicly available JAFFE dataset. Experimental data shows the efficient and robust nature of our framework and shows its uniqueness on comparing it with other existing available methods.
Mayur Rahul Rati Shukla Puneet Goyal Zaheer Abbas Siddiqui Vikash Yadav
209099994
Analysis of PCA and LDA Features for Facial Expression Recognition Using SVM and HMM Classifiers
The face of a person is full of emotions. The emotions can be used to interact between human beings and computers. Different systems have been developed to recognize emotions. In this paper, a facial expression recognition system has been developed to recognize emotions which have four steps mainly face image acquisition, pre processing, feature extraction and face recognition with six emotions. In the first step, a single face image was selected as an input. In the second step, this face image was converted into HSV color space and median filtering was used to remove the noise. Two methods namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been used for feature extraction and representation in the third step. During the fourth step, the Support Vector Machine (SVM) and Hidden Markov Model (HMM) have been used to classify the given face over two different datasets. Finally, the experiments were performed by combining two feature vectors to recognize the face and the proposed system is able to recognize six basic emotions viz. happiness, sadness, anger, disgust, surprise and fear. The performance has been evaluated and presented using confusion matrix and classification accuracy. The combination of features from PCA and LDA with SVM is effective and useful for classification and recognition of facial expressions.
Techno Societal 2018
S Varma Megha Shinde Satishkumar S Chavan
108382944
Layered Recognition Scheme for Robust Human Facial Expression Recognition using modified Hidden Markov Model
Facial Expression Recognition is an application used for biometric software that can be used to recognize special expressions in a digital image by analysing and comparing different patterns. These software are popularly used for the purpose of security and are commonly used in other applications such as credit card verification, surveillance systems, medicines, home security, human computer interface, etc. Recognizing faces becomes very difficult when there is a frequent change occurs in facial expressions. In this paper two layer extension of HMM is used to identify continuous effective facial expressions. Partition technique is used for feature extraction. Two layered extension of HMM consists of bottom layer which represents the atomic expression made by eyes, nose and lips. Further upper layer represents the combination of these atomic expressions such as smile, fear etc. In HMM, Baum Welch, Viterbi and Forward methods are used for parameter estimation for calculating the optimal state sequence and probability of the observed sequence respectively. This proposed system consists of three level of classification. Output of the first level is used for the training purposes for the second level and further this level is used for the third level for testing. Six basic facial expressions are recognised i.e. anger, disgust, fear, joy, sadness and surprise. Experimental result shows that Proposed System performs better than normal HMM and has the overall accuracy of 85% using JAFFE database.
J. Multim. Process. Technol.
Mayur Rahul Rashi Agrawal Narendra Kohli
182335684
Zernike Moment Based Facial Expression Recognition Using Two Staged Hidden Markov Model
Facial expression recognition is an application used for biometric software that can be used to recognize special expressions in a digital image by analysing and comparing different patterns. This software is popularly used for the purpose of security and is commonly used in other applications such as medicines, home security, human computer interface, credit card verification and surveillance systems. Recognizing faces becomes very difficult when there is a frequent change occurs in facial expressions. In this paper, new improved extension of HMM is used to identify continuous effective facial expressions. Extraction of features is done using Zernike moments. The new improved version of HMM consists of two stages: first stage denotes the expression made by individual features like nose, mouth, etc. and second stage denotes the mixture of individual features like smile, fear, etc. Seven universal facial expressions are recognized, i.e. sadness, surprise, neutral, anger, disgust, fear and joy. Experimental result shows the better performance of the proposed system using JAFFE data sets.
Mayur Rahul Rati Shukla Dinesh Kumar Yadav Vikash Yadav
69800078
An Efficient Technique for Facial Expression Recognition Using Multistage Hidden Markov Model
Partition based feature extraction is widely used in the pattern recognition and computer vision. This method is robust to some changes like occlusion, background, etc. In this paper, partition based technique is used for feature extraction and extension of HMM is used as a classifier. The new introduced multistage HMM consists of two layers. In which bottom layer represents the atomic expression made by eyes, nose, and lips. Further upper layer represents the combination of these atomic expressions such as smile, fear, etc. Six basic facial expressions are recognized, i.e. anger, disgust, fear, joy, sadness, and surprise. Experimental result shows that proposed system performs better than normal HMM and has the overall accuracy of 85% using JAFFE database.
Mayur Rahul Pushpa Mamoria Narendra Kohli Rashi Agrawal
139104826
Typing Technology of Virtual Character of Animation Based on Enneagram Personality
Facial expression inevitably leads to facial deformation. In this paper, the advantages and disadvantages of feature extraction and recognition method are considered. A facial expression recognition method based on mixed feature fusion of nine types of personality is proposed. The texture features are extracted by discrete wavelet transform and standard orthogonal non negative matrix decomposition for a person's facial expression image sequence, and AAM square is used. The method calculates the coordinate difference between the expression key points of the expression frame and the neutral frame in the image sequence, and extracts the geometric deformation characteristics. Then use the canonical correlation analysis (CCA) to fuse the two features, and finally use discrete HMM to classify faces.
Trans. Edutainment
Jiang-lin Luo Mei-li Zhu Qing-qing Wang
53293508
Facial expression recognition using best tree RD LGP encoded features and HMM
Hand and face gestures enable deaf people to communicate in their daily lives rather than speaking. This paper describes a hidden Markov model (HMM) based framework for face sign recognition and de.
Int. J. Wavelets Multiresolution Inf. Process.
Ahmed A Nashat
51905017
An Efficient and Robust Technique for Facial Expression Recognition Using Modified Hidden Markov Model
Partition based feature extraction is widely used in the pattern recognition and computer vision. This method is robust to some changes like occlusion, background, etc. In this article, a partition based technique is used for feature extraction and extension of HMM is used as a classifier. The new introduced multi stage HMM consists of two layers. In which bottom layer represents the atomic expression made by eyes, nose and lips. Further, the upper layer represents the combination of these atomic expressions such as smile, fear, etc. Six basic facial expressions are recognized, i.e. anger, disgust, fear, joy, sadness and surprise. Experimental results show that the proposed system performs better than normal HMM and has an overall accuracy of 85% using the JAFFE database.
Int. J. Appl. Evol. Comput.
Mayur Rahul Pushpa Mamoria Narendra Kohli Rashi Agrawal
53279156
HMM Based Emotion Detection in Games
There has been always this question asked, do games really need emotion intelligence? With the advances in artificial intelligence and great researches in computer internal structure, gaming design has been evolved to emotional detections and feedback. Again a question comes here, what if gaming characters recognize and feel your emotions and change the gameplay as per your mood? In this paper we have presented the Hidden Markov Model (HMM) based real time player's emotion recognition system. The input of the model is video of six emotions as joyful, rage, astonish, antipathy, scared and sorrow. The work employs existing methods and proposes a system model of HMMs for automatically splitting and identifying facial expression(emotions) from video sequences.
2018 3rd International Conference for Convergence in Technology (I2CT)
Prerna Mishra Prof Saurabh Ratnaparkhi
4379937
Independent Bayesian classifier combination based sign language recognition using facial expression
Abstract Automatic Sign Language Recognition (SLR) systems are usually designed by means of recognizing hand and finger gestures. However, facial expressions play an important role to represent the emotional states during sign language communication, has not yet been analyzed to its fullest potential in SLR systems. A SLR system is incomplete without the signer's facial expressions corresponding to the sign gesture. In this paper, we present a novel multimodal framework for SLR system by incorporating facial expression with sign gesture using two different sensors, namely Leap motion and Kinect. Sign gestures are recorded using Leap motion and simultaneously a Kinect is used to capture the facial data of the signer. We have collected a dataset of 51 dynamic sign word gestures. The recognition is performed using Hidden Markov Model (HMM) Next, we have applied Independent Bayesian Classification Combination (IBCC) approach to combine the decision of different modalities for improving recognition performance. Our analysis shows promising results with recognition rates of 96.05% and 94.27% for single and double hand gestures, respectively. The proposed multimodal framework achieves 1.84% and 2.60% gains as compared to uni modal framework on single and double hand gestures, respectively.
Inf. Sci.
Pradeep Kumar Partha Pratim Roy Debi Prosad Dogra