Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder

arXiv:1710.02856v1 [] 8 Oct 2017

Maneet Singh1 , Shruti Nagpal1 , Mayank Vatsa1,2 , Richa Singh1,2 , Afzel Noore2 , and Angshul Majumdar1
1
IIIT-Delhi, India, 2 West Virginia University
{maneets, shrutin, mayank, rsingh, angshul}@iiitd.ac.in, afzel.noore@mail.wvu.edu

Abstract
Soft biometric modalities have shown their utility in different applications including reducing the search space significantly. This leads to improved recognition performance,
reduced computation time, and faster processing of test
samples. Some common soft biometric modalities are ethnicity, gender, age, hair color, iris color, presence of facial
hair or moles, and markers. This research focuses on performing ethnicity and gender classification on iris images.
We present a novel supervised autoencoder based approach,
Deep Class-Encoder, which uses class labels to learn discriminative representation for the given sample by mapping
the learned feature vector to its label. The proposed model
is evaluated on two datasets each for ethnicity and gender classification. The results obtained using the proposed
Deep Class-Encoder demonstrate its effectiveness in comparison to existing approaches and state-of-the-art methods.

1. Introduction
Soft biometric traits are physical or behavioral characteristics that can be extracted from the human body which help
differentiate individuals from one another. However, they
often lack information that is permanent or sufficiently discriminative to uniquely identify an individual. These traits
are valuable as they aid in recognition and enhance the performance of automated biometric systems by reducing the
search space. This results in a significant decrease of testing time as well. Some of the most commonly used soft
biometric modalities are gender, ethnicity, gait, age, height,
hair color, eye color, and presence of moles or mustache on
the face [4, 16]. Figure 1 presents few sample soft biometric
modalities that can be extracted from face and iris.
Iris texture has emerged as one of the most robust
and widely used biometric modality for automated identity
recognition [15]. Studies in literature have also established
the presence of similar information in iris patterns to discriminate between ethnicities and gender [4, 10, 11, 21, 22].
That is, samples belonging to the same ethnicity, have some

Face Markers

Ethnicity

Skin Color

Age
Iris Color

Gender

Iris

Facial Hair

Face

Figure 1: Sample soft biometric modalities that can be extracted from face and iris images. All images have been
taken from the Internet.
commonalities in the iris pattern, and the ones belonging to
the same gender also share certain information.
In literature, gender from iris images was first predicted
by Thomas et al. [23] using an in-house dataset of left irises
only. The iris images were segmented and normalized, onto
which log-Gabor filter was applied to obtain the iris texture information. Features like mean, standard deviation,
and local variation were computed for different neighborhoods of the texture pattern. Seven additional geometric
features were also computed, followed by projection onto a
random subspace ensemble of trees for classification. Lagree and Bowyer [11] extended this work by computing
texture features for different horizontal and vertical bands
of the normalized iris image. A total of 630 features were
computed for 14 regions in a given image. These features
were used as input to the Sequential Minimum Optimization
algorithm for classification. The same extracted features
were also used to perform ethnicity classification of iris images. Tapia et al. [21] used uniform LBP with concatenated
histograms to perform gender classification on iris images
on an in-house UND dataset. However, in this work, no
mutual exclusivity of subjects was maintained between the
training and testing partitions. Building upon this, in 2016
[22], the disjoint train-test sets in terms of the subject were
created, and gender classification was performed using the

same binary texture pattern that is used for identity recognition. This work presented results on three datasets, the UND
dataset used in [21], a novel ND-Gender From Iris (NDGFI) dataset, and a subject disjoint validation set (UND V).
Qiu et al. [18] first proposed the idea of classifying iris
images into ethnicity based categories. The authors combined images from three datasets and performed classification between Asian and Non-Asian by computing the Gabor energy ratio between two regions of the iris. Continuing this work, a method to represent iris images using iris
textons was developed [19]. The authors performed classification between Asian and Non-Asian on the CASIA BioSecure dataset. A filter bank of 40 Gabor filters was created
and a 40-dimensional feature vector was computed. Each
iris image was represented with a histogram of iris textons,
which was used as a feature for SVMs to perform classification. The authors reported an accuracy of 91.02% to
classify Asians v/s non-Asians. However, it is essential to
note that these results were demonstrated on images collected from different datasets for different ethnicities, thus
incorporating inter-database variations at the time of classification. Lagree and Bowyer [11] proposed to identify the
ethnicity using features obtained from several bands for 14
subregions of the iris. In 2012, Zarei and Mou [25] used artificial neural networks to predict the ethnicity. The authors
computed a total of 882 features from various regions of the
iris, which were combined to create a feature vector used as
input to a neural network for classification.
This research presents ethnicity and gender classification
from iris images, using the proposed Deep Class-Encoder.
It is an autoencoder based supervised model which utilizes
the robust feature extraction capabilities of deep learning
and discriminative capabilities of supervised models. The
research contributions of this work are two-fold:
• An autoencoder based supervised model, Deep ClassEncoder is presented. The proposed model incorporates
supervision by learning weights such that the hidden representation is mapped onto its class label. The proposed
model is optimized using Alternating Direction Method
of Multipliers [3], instead of a gradient descent approach,
thus reducing the training time substantially.
• For gender classification, results are reported on existing protocols of ND-Iris-0405 [17] and ND-Gender From
Iris [22] datasets. Ethnicity classification is performed on
ND-Iris-0405 and the proposed Multi-Ethnicity dataset.
A new protocol is also defined on the Multi-Ethnicity
dataset for ethnicity classification. The proposed model
achieves state-of-the-art results for each task.

2. Proposed Deep Class-Encoder
The success of unsupervised feature learning architectures has been well established over the past couple of years,

especially with the advent of representation learning techniques such as Deep Learning [2]. The performance of
these architectures is not restricted to a particular domain,
and spans across areas such as speech recognition, biometric recognition, computer vision, and natural language processing [13]. Unsupervised architectures aim to harness the
information provided from unlabeled data to learn representative features used for classification at a later stage.
Autoencoders are deep learning architectures used to
learn representations in an unsupervised manner. A traditional autoencoder consists of two sub-systems, encoder
and decoder. For a given input X, the encoder is used to obtain the representation H using the learned encoder weights
We . The decoder is used for reconstructing the input from
the learned representation using the decoder weights, Wd .
The loss function of an autoencoder is optimized by minimizing the difference between the given input data, X, and
the reconstructed data, X̂. It is mathematically expressed
as:
2
min X − X̂
(1)
F

where, the input consists of n training samples, the hidden
representation is computed as H = φ(We X), and the reconstructed data X̂ = Wd H. φ is an activation function
which can be any non-linear function such as sigmoid, or
tanh or linear activation corresponding to unit activation.
Upon expanding Equation 1, with unit activation function,
the loss function can be expressed as:
2

arg min kX − Wd We XkF

(2)

Wd , We

A traditional biometrics pipeline consists of feature extraction, followed by classification. The feature extraction
module extracts meaningful representations from the input,
while the classifier learns a boundary in order to distinguish
between the representations. Autoencoders have been used
as unsupervised models for feature extraction in several applications. In order to learn meaningful representations for
specific tasks, researchers have proposed incorporating supervision in the autoencoder model. Most of the existing
supervised models do not explicitly encode the class label,
but encode only the class information (same/different) for
feature learning [6, 14, 26]. Encoding class information often leads to reducing the intra-class variability.
In this research, we incorporate supervision in the feature
extraction module in an attempt to introduce discriminability at the time of feature extraction itself. It is our hypothesis
that feature extraction from such a model would facilitate
better classification. We propose a model which utilizes the
robust feature extraction capability of autoencoders, while
learning discriminative features to enhance classification.
The proposed architecture, termed as Class-Encoder is built
such that the class label of a given training sample is encoded in the learned feature representation, thereby making

the model supervised in nature. This is done by incorporating an additional mapping matrix M, which maps the
hidden representation We X onto it’s corresponding class
label C. Mathematically, it can be expressed as:
2

min kC − MWe XkF

(3)

M

where, C ∈ Rl×n (l being the number of distinct classes) is
a binary vector with its ith element set to 1 if the data sample belongs to class i, rest being zero. Matrix M captures
the linear mapping between the feature vector (We X) and
it’s corresponding class label. Thus, under a fixed M and
We , samples belonging to the same class should map to the
same class label. Extending Equation 2 to incorporate the
above term, the loss function of the proposed Class-Encoder
can thus be given as:
min

2

M, Wd , We

Wd

We

Hidden representation
h = Wex
Input vector, x

(a) Traditional Unsupervised Autoencoder

Wd

We

2

kX − Wd We XkF + λ kC − MWe XkF

(4)
where, the regularization parameter, λ, controls the relative
contribution of the two terms. In Equation 4, the first term
corresponds to the loss function of the traditional autoencoder. It facilitates learning of parameters such that the reconstruction error is minimized and a meaningful representation is learned. The second term incorporates supervision
in the autoencoder formulation and enables the model to
learn a discriminative representation of the input data. The
weight matrix is learned such that upon projecting the input
data on it, the representation maps to a specific class label, thus encoding class-specific information in the feature
learning process. Figure 2 gives an overview of the proposed model, and it’s comparison with the traditional unsupervised autoencoder.
Equation 4 represents a least square formulation, however, the large size of matrices involved and the nonconvexity of the problem increases the computational costs.
To mitigate the same, the Majorization-Minimization (MM)
technique [5] along with Alternating Direction Method of
Multipliers (ADMM) [3] is utilized. The aim of the MM
technique is to replace complex equations with simpler and
easier optimization steps. Moreover, ADMM does not involve computing the derivatives at each epoch, which leads
to significant lower training time as compared to a traditional Stacked Autoencoder.
For a k layer model of Deep Class-Encoder, Equation 4
can be extended as follows:

Reconstructed sample
𝑥෤ = WdWex

Reconstructed sample
𝑥෤ = WdWex
Hidden representation
h = Wex

M

Input vector, x

Class label vector
v = MWex

(b) Proposed Class-Encoder

Figure 2: We and Wd correspond to the encoding and decoding weights respectively, while M captures the linear
mapping between the feature vector and its class label.
k th layer, respectively. Xk refers to the input to the k th
layer of the model, which is defined as Wek-1 Xk-1 . At the
first layer, X1 = X, that is, the input data. Thus, for a klayer model, the proposed Class-Encoder aims to learn discriminative representations at each layer by modeling the
class label information during the feature learning process.

2
+
F

3. Ethnicity and Gender Classification using
Deep Class-Encoder

(5)
where, Wek , Wdk , and Mk correspond to the encoding
weights, decoding weights and the mapping matrix of the

Deep Class-Encoder is used for the task of gender and
ethnicity classification of iris images. For both the problems, experiments are performed on two datasets each. Details regarding the algorithm, datasets used, and experimental protocols are given below.

min

M, Wd , We

X − (Wd1 Wd2 ...Wdk (Wek ...We2 We1 X))
k
X

λ C − Mi (Wei Xi )

2
F

i=1

Female

Female Asian

Chinese

Male

Male White

Indian

(a) ND-GFI Dataset

(b) ND-Iris-0405 Dataset

(c) Multi-Ethnicity Iris Dataset

Figure 3: Sample images from the three datasets used for performing gender and ethnicity classification on iris images.

3.1. Ethnicity Classification
The proposed Deep Class-Encoder is used to perform
ethnicity classification using Equation 5. Here, X is a m×n
matrix which contains the input images, where m is the size
of the input image and n is the number of input samples.
In this case, iris images captured in the NIR spectrum are
given as input, resized to 48 × 64, therefore m being equal
to 3,072. Wd , We are the weight parameters learned at the
time of training. C is the class label matrix of dimension
l × n, where l is the number of distinct classes. A two
layer Deep Class-Encoder of dimension [3072, 768, 768] is
learned. In order to evaluate the efficacy of the proposed
model, results and comparison have been shown with two
classifiers: Random Decision Forest (RDF) and a two layer
Neural Network (NNet) having dimension [768, 192, 48].
3.1.1

Datasets and Experimental Protocol

Two datasets, ND-Iris-0405 [17] and a combined MultiEthnicity Iris Dataset, have been used in this research.
ND-Iris-0405 dataset consists of 64,980 images corresponding to 356 subjects, out of which 158 subjects are
Caucasian (White), 82 are Asian, and the remaining 24 belong to other ethnicities. Since the number of samples belonging to other ethnicity are very limited, images corresponding to the Caucasian and Asian subjects have been
chosen for evaluation, thereby resulting in a two-class problem (Caucasian or Asian), consisting of 60,259 images. Out
of these, 26,272 images (containing equal number of Caucasian and Asian images) are used for training, while the
remaining images form the test set.
Multi-Ethnicity Iris dataset is created by combining three
existing datasets, due to the lack of publicly available iris
datasets containing images of multiple ethnicities. The
dataset consists of images from:
• CASIA-Iris V3 dataset [1] collected by the Chinese
Academy of Sciences’ Institute of Automation (CASIA):
The dataset consists of three parts, CASIA-Iris-Interval,

Table 1: Experimental protocols for ethnicity and gender
classification.
Dataset

No. of Training No. of Testing
Images
Images
Ethnicity Classification
ND-Iris-0405 [17]
26,272
33,987
Multi-Ethnicity Iris
1,302
59,008
Gender Classification
ND-Iris-0405 [17]
42,899
22,081
ND-GFI [22]
2,399
600
UND V [22]
1,944
CASIA-Iris-Lamp, and CASIA-Iris-Twin. Out of these,
CASIA-Iris Lamp and CASIA-Iris Twin contain 18,197
images of only Chinese ethnicity, that have been used.
• IMP dataset [20]: This dataset contains images collected
in the visible and NIR spectrum, and night-time mode.
Out of these, 630 images of Indian ethnicity captured in
the NIR spectrum are used.
• ND-Iris-0405 dataset [17]: As mentioned previously, this
dataset contains images of subjects belonging to Asian,
Caucasian, or other ethnicities. Since Asian might correspond to Chinese or Indian as well, and further categorization has not been provided in the dataset, we only
use 41,518 images pertaining to Caucasian subjects in the
Multi-Ethnicity Iris dataset.
Therefore, the Multi-Ethnicity Iris dataset consists of
60,310 images of Chinese, Indian, and Caucasian ethnicities. 1,302 images are chosen for creating the training partition, which consists of equal number of images from all
three classes (Chinese, Indian, and Caucasian), and the remaining images are used to create the test set.

3.2. Gender Classification
Deep Class-Encoder is also used for performing gender
classification on iris images. Similar to the previous experiments, the images are down-sampled to 48 × 64. Thus, X is

Algorithm
Stacked Autoencoder [8]
Stacked Denoising Autoencoder [24]
Deep Belief Network [7]
Discriminative RBM [12]
Proposed Deep Class-Encoder

RDF
NNet
87.24
80.05
85.20
64.51
85.12
87.43
90.33
89.35
94.33

of dimension 3072 × n, where n is the number of input images. C is a 2 × n binary-matrix containing the class labels.
The architecture of the feature extractors and classifiers is
kept consistent with that described in Section 3.1.
3.2.1

Table 3: Ethnicity classification accuracy (%) on the MultiEthnicity Iris dataset with two classifiers: RDF and NNet.
Algorithm
Stacked Autoencoder [8]
Stacked Denoising Autoencoder [24]
Deep Belief Network [7]
Discriminative RBM [12]
Proposed Deep Class-Encoder

RDF
NNet
95.76
90.42
95.13
87.3
96.97
95.22
95.92
97.22
97.38

Table 4: Confusion matrix of Deep Class-Encoder on the
ND-Iris-0405 dataset for ethnicity classification.

Actual

Table 2: Ethnicity classification accuracy (%) on the NDIris-0405 dataset with two classifiers: Random Decision
Forest (RDF) and Neural Network (NNet).

Asian
Caucasian

Predicted
Asian
93.78%
5.12%

Caucasian
6.22%
94.88%

Datasets and Experimental Protocol

Experiments have been performed on two datasets: NDIris-0405 [17] and ND-Gender From Iris (GFI) [22].
ND-Iris-0405 dataset consists of 64,980 images corresponding to 158 females and 198 males. 70% data of each
class is used for training (resulting in 42,899 images), while
the remaining form the test set.
ND-Gender From Iris (ND-GFI) dataset consists of
3,000 images from 750 males and 750 females. The dataset
consists of a pre-defined protocol [22], where 80% of the
data (per class) is used for training, and the remaining 20%
is used for testing. A separate subject-disjoint set (UND V)
containing 1,944 images has also been provided for evaluating the performance of the trained model. The same protocol is followed for the evaluation of the proposed model.
All protocols ensure mutually exclusive training and testing
sets, such that there is no image which occurs in both the
partitions. Figure 3 presents sample images from all three
datasets, and Table 1 summarizes the protocols followed for
the experimental evaluation.

4. Experimental Results and Analysis
The proposed model has been compared with existing
Deep Learning models for performing gender and ethnicity classification on iris images. Comparison has been
performed with Stacked Autoencoder (SAE), Stacked Denoising Autoencoder (SDAE) [24], Deep Belief Network
(DBN) [7], Discriminative Restricted Boltzmann Machine
(DRBM) [12], and AlexNet [9], i.e. a Convolutional Neural
Network based model. To be consistent with the proposed
model, feature extraction models (SAE, SDAE, DBN) have
the same architecture as that of the proposed Deep ClassEncoder (described in Section 3.1). This is followed by
learning a classifier (RDF or NNet) for classification. Owing to the class imbalance in the test samples, mean classwise accuracy has been reported throughout this paper.

Since no existing Commercial-Off-The-Shelf system predicts the attributes of gender and ethnicity for iris images,
a comparison could not have been drawn for the same.
Task specific analysis for the proposed Deep Class-Encoder
model is given below.

4.1. Ethnicity Classification
Tables 2 and 3 present the accuracy for ethnicity classification on ND-Iris-0405 and Multi-Ethnicity Iris dataset,
respectively. Figure 4 also presents the Receiver Operative Characteristic (ROC) curve obtained on the ND-Iris0405 dataset for ethnicity classification of Asians versus
Caucasians. On the ND-Iris-0405 dataset, the proposed
model achieves a classification accuracy of 89.35% and
94.35% with RDF and NNet classifier, respectively (Table 2). Compared with other unsupervised feature learning models (SAE, SDAE, and DBM), Deep Class-Encoder
presents an improvement of at least 7%. Direct comparison
with SAE (difference of around 14% in accuracy) shows
the benefit of encoding class labels in the proposed model.
Comparison with Discriminative RBM, which is a supervised Deep Learning architecture, also presents an improvement of 4%. The key difference between Discriminative
RBM and proposed Deep Class-Encoder is that the former models the joint probability of the sample and the label, while the later tries to learn representations such that
under a fixed learned mapping, the hidden representations
map to the class labels. It can thus be observed that the
introduction of the mapping matrix facilitates learning of
discriminative features. Table 4 gives the confusion matrix obtained with the proposed Deep Class-Encoder and
Neural Network. The low mis-classification rates for both
the classes suggest that the proposed model is able to encode meaningful and discriminative representations across
classes.
Similar results can be observed from Table 3 for eth-

1
DBN
DRBM
SAE
SDAE
Proposed

0.9

True Positive Rate

0.8
0.7

Indian

0.6

Caucasian

Figure 6: Sample images mis-classified by the proposed
Deep Class-Encoder and Neural Network model for the
Multi-Ethnicity Iris dataset. Artifacts such as hair (bangs)
and incomplete iris information result in mis-classification.
Table 5: Gender classification accuracy (%) on the ND-Iris
0405 dataset with two classifiers: RDF and NNet.

0.5
0.4
0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

False Positive Rate

Figure 4: Ethnicity classification on the ND-Iris-0405
dataset. For all models, the best performance between RDF
and NNet is plotted.
Classification Accuracy (%)

Chinese

100
95
90
85
80
75
70

Algorithm
Stacked Autoencoder [8]
Stacked Denoising Autoencoder [24]
Deep Belief Network [7]
Discriminative RBM [12]
Proposed Deep Class-Encoder

RDF
NNet
74.73
66.71
69.23
56.07
75.74
76.03
70.76
80.06
82.53

evaluation. While there exists works utilizing the ND-Iris0405 dataset, however, experiments are performed on a very
small subset of the dataset [10].
Caucasian

Chinese

Indian

Ethnicity

SAE

SDAE

DBN

DRBM

Proposed

Figure 5: Classification performance (%) of all algorithms
on all three classes for the Multi-Ethnicity Iris dataset.
nicity classification on Multi-Ethnicity Iris dataset. In this
case, the classifier aims to classify the images as either Caucasian, Chinese, or Indian. The proposed model achieves a
classification accuracy of 97.38% (with Neural Network),
which shows improvement over other comparative feature
extraction models. Moreover, the variation in classification
performance obtained with RDF and NNet for Deep ClassEncoder in within 1%, whereas the difference is as high as
8% for other comparative models (SDAE). This observation further instantiates that the proposed model learns robust features which are independent of the classifier used in
the pipeline. Figure 5 gives the bar graph with classification
accuracies of all models for all three classes. The proposed
model achieves at least 96% classification accuracy for all
three classes, thus promoting the feature learning process of
Deep Class-Encoder. Interestingly, the model mis-classifies
only one sample of Indian ethnicity. As shown in Figure 6,
overall illumination and size variations along with the presence of artifacts such as hair bangs render samples challenging for ethnicity classification.
It is important to note that no direct comparison could be
drawn with existing work on ethnicity classification, since
there is no fixed protocol or datasets which are used for

4.2. Gender Classification
Tables 5 and 6 present the gender classification accuracy
on ND-Iris-0405, and ND-GFI dataset, respectively. Figure 7 also presents the ROC curves obtained on both the
datasets. On the ND-Iris-0405 dataset, the proposed Deep
Class-Encoder achieves a classification accuracy of 82.53%
(with Neural Network), which is at least 5.5% better than
other comparative models. Figure 9 presents sample images mis-classified by the proposed model. It can be observed that iris images having poor illumination or partial
information serve as challenging samples.
Experiments are also performed on the ND-GFI dataset,
where, the Deep Class-Encoder achieved an accuracy of
83.17%, showing at least 5% improvement over other deep
learning based comparative models. In literature, Tapai et
al. [22] achieve an accuracy of 84.83% on the same protocol, which means that the proposed model performs 10 extra mis-classifications. The proposed Deep Class-Encoder
achieves a classification accuracy of 79.25% on the UND V
dataset. This dataset is provided with the ND-GFI dataset
and models the real world scenario of disjoint subjects in
the training and testing set. The protocol mentioned by the
authors [22] has been followed for evaluation, using which
they report a classification accuracy of 77.5% (state-of-theart for this dataset). This results in a difference of 34 correctly classified samples, thereby promoting the generalization abilities of Deep Class-Encoder.
Figure 8 presents a bar graph with the classification accuracies of a CNN based feature extractor, AlexNet [9], Tapia
et al. [22], and the proposed model. Comparison with Con-

1

1

0.9

0.9
DBN
DRBM
SAE
SDAE
Proposed

0.7

DBN
DRBM
SAE
SDAE
Proposed

0.8

True Positive Rate

True Positive Rate

0.8

0.6
0.5
0.4

0.7
0.6
0.5
0.4

0.3

0.3

0.2

0.2

0.1

0.1
0

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

False Positive Rate

False Positive Rate

(a) ND-Iris-0405 Dataset

(b) ND-GFI Dataset

Classification Accuracy
(%)

Figure 7: ROC curves for gender classification. For each model, the best performing classifier (RDF or NNet) is plotted.
100
80
60

Males classified as Females

40
20
0
AlexNet

Tapia et al.

Proposed

Algorithm

Figure 8: Bar graph presenting the gender classification accuracy obtained on the UND V dataset [22].
Table 6: Gender classification accuracy (%) on the ND-GFI
dataset with two classifiers: RDF and NNet.
Algorithm
Stacked Autoencoder [8]
Stacked Denoising Autoencoder [24]
Deep Belief Network [7]
Discriminative RBM [12]
Proposed Deep Class-Encoder

RDF
NNet
64.17
65.33
71.33
66.00
67.17
71.83
78.67
78.17
83.17

volutional Neural Network based feature extractor, AlexNet
[9] on the UND V dataset further promotes the utility of
the proposed model. Upon using the pre-trained model
for feature extraction only, the proposed model achieves at
least 16% improved performance. A major advantage of
the proposed Deep Class-Encoder is in terms of the training
time. As compared to Stacked Autoencoders, Deep ClassEncoder takes one fourth the total training time. This computational advantage is primarily due to the MajorizationMinimization and Alternating Direction Method of Multipliers based optimization which does not involve computing
derivatives at each epoch.

Females classified as Males
Figure 9: Sample mis-classifications by the proposed
model. It can be observed that incomplete, poorly illuminated, and hidden iris tend to be more difficult to classify.

5. Conclusion
This research addresses the task of gender and ethnicity classification of iris images. A supervised autoencoder
model, termed as Deep Class-Encoder has been proposed
for the given task. Deep Class-Encoder utilizes the class
labels at the time of feature learning, in order to learn discriminative features. The efficacy of the proposed model
is evaluated on two datasets each, for gender and ethnicity classification. Experimental evaluation and results further promote the utility of the proposed model for learning
class-specific discriminative features.

6. Acknowledgment
This research is partially supported by MEITY (Government of India), India. S. Nagpal is partially supported
through TCS PhD fellowship. M. Vatsa, R. Singh, and A.
Majumdar are partially supported through Infosys Center
for Artificial Intelligence. We also thank NVIDIA Corp.
for Tesla K40 GPU for research.

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