Vol.:(0123456789)

Wireless Personal Communicationshttps://doi.org/10.1007/s11277-020-07700-9

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Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) Descriptor for Iris Liveness Detection

Rohit Agarwal1 · Anand Singh Jalal1 · K. V. Arya2

© Springer Science+Business Media, LLC, part of Springer Nature 2020

AbstractBiometric traits are frequently used by security agencies for automatic recognition of a person. There are numerous biometric traits used for person identification. In recent years, iris biometric trait becomes very popular and efficient in many security applications. How-ever, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a genuine trait. The effect of an artificial artifact of a humanoid iris could be in the form of contact lens attack and print attack make difficult the expected policy of a biometric liveness system. In this paper, the different and enhanced feature descriptor has been proposed i.e. Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) for forged iris detection. The relationship between the center pixel and its hexa neighbor has been explored by the suggested descriptor. The Proposed approach is tested on ATVS-FIr DB and IIIT-D CLI database for iris liveness detection and the results show better results for liveness detection in term of accuracy and average error rate.

Keywords Spoof attack · Feature descriptor · Forged iris liveness detection

1 Introduction

Physiological or behavioural characteristics of an individual decide the automatic recognition of individual which is referred as biometric recognition [1]. Biometrics strengthens the security of any systems by using different physiological and behav-ioural biometric traits. In view of present scenario of the pandemic COVID-19, special care about hygiene needs to be taken. Fingerprint biometric pose danger as it could lead to carrier of the infection. As wearing of mask has become mandatory he accuracy

* Rohit Agarwal rohit.agrwal@gla.ac.in

Anand Singh Jalal asjalal@gla.ac.in

K. V. Arya kvarya@iiitm.ac.in

1 Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh 281406, India

2 Department of I.C.T, ABV-IIITM, Gwalior, Madhya Pradesh 474010, India

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of face recognition biometric is also at stack. So iris biometric is the most hygienic accurate and reliable modality that can be used these days.

In genuine situations, various sorts of mocking assaults are introduced to disregard the security of a biometric framework [2]. The greater part of the computerized assur-ance strategies is not viable to identify the satirizing assaults, for example, high class printed iris pictures or contact-focal point assault. Iris liveness identification frame-work hopes to ensure that an introduced picture is from a live subject as opposed to an iris printed photograph, a glass eye and a contact focal point printed iris picture.

The iris is a lean, round configuration in the eye. Diameter and size of the pupil are controlled by iris, which fixes the amount of light finally received by  the retina. Iris only defines eye colour. In ocular terms, the iris is the diaphragm while the pupil is the eye’s aperture [3].

Best in class approaches are examined in the literature to group the phony and gen-uine individual based on contact-focal point assault or photograph assaults utilizing static features. For the assessment of the iris quality in an iris classification framework, we need a robust descriptor.

For texture illustration of every area of iris picture Local Binary Patterns (LBP) [4] is utilized. SIFT descriptor is utilized [5] to dissect the highlights which are extri-cated from weighted LBP. Galbally et  al. [6] proposed a set of twenty-five integral picture quality measures.LDA and QDA classifier are utilized to characterize genuine and counterfeit. However, the methodologies were complex just for photograph assault with high multifaceted nature.

A Multi-Scale Binarized Statistical Image Feature Extraction (M-BSIF) is used for feature extraction [7]. After that eight autonomous SVM classifiers are utilized for all scale. It is approved on just photograph assault and bombs if there should be an occur-rence of contact focal point assault and forged eye assault. Hu et al. [8] proposed rec-ognition system which misuses the relationship of the highlights in neighboring area. In most of the methods discussed above the total error rate is high on account of low-level features. Dubey and Singh [9] suggested a descriptor with high curse of dimen-sionality. The proposed research work is inspired by the information that there is a variance in the iris texture excellence between a real and a fake iris [10].

Curse of dimensionality and relation between center pixels with hexa neighbors is the main issue in the state-of-the-art descriptors. Agarwal et al. [11] proposed a local descriptor (LBHxEP) that describes the superior order derivative space with a high curse of dimensionality. We propose a productive descriptor for the nearby element by making a connection between the middle pixel and its hexa neighbor. The cells of a colony honeycomb are hexagonal and it utilizes space. Hexagonal shape is prefer-able because of its shape with higher symmetry, consistent network and higher angular resolution. “Curse of dimensionality” problem can be solved by using the proposed descriptor and it shows better results under dissimilar kinds of the spoofing attack. ATVS-FIr DB and IIIT-D CLI database are used to validate the robustness of the pro-posed descriptor is tested on. For ATVS dataset the Average Error Rate (AER) is 1.6 and for IIIT-D dataset 0.58 correspondingly.

In the remaining section of this state-of-the-art is defined in Sect. 2. Section 3 pre-sents the proposed descriptor, Sect. 4 presents the evaluation and experimental results and Sect. 5 has conclusion of this paper.

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2 Related Work

As of late, the developing consideration for the evaluation of biometric liveness detection system has provoked the innovation of different critical attacks. The safety of biometric framework attracts the researchers to propose various prompt, accurate and efficient detec-tion systems. The main objective of various biometric detection systems is to improve the robustness against various kind attacks. In such attacks, attackers develop fake biometric traits to access the genuine client. The attackers usually develop fake iris photo, fake iris contact lens and artificial eye for iris biometric system [12] as shown in Fig. 1 [13].

Biometric authentication is considered to be one of the most reliable authentication system, therefore these authentication systems are capable enough of honestly classify the genuine and forged biometric traits. The main concern of biometric authentication system is to strengthen and improve the security of liveliness detection. Various anti-spoofing sys-tems are categorized in two basic classes [12] as represented in Fig. 2.

(a) Based on Sensor: Electronic instrument or sensors for sensing.(b) Based on Features: Texture features used to categorize the real and fake identity.

For the proposed work, various iris liveness discovery strategies based on static high-lights systems has been explored. The iris made up of rich texture having of “pectinate

Fig. 1 Different Types of Attacks

Fig. 2 Anti Spoofing Methods

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ligaments adhering into a tangled mesh revealing striations, ciliary processes, crypts, rings, furrows, a corona, sometimes freckles, vasculature and other features” [14].

He et al. [15] determines statistical features like contrast, ASM, mean and SD; where classification has been performed by SVM. This method has been implemented with limited dataset and restricted to photo attack. Further He et al. [10] analysis the image quality by 2-D fourier spectra as statistical features selected in [15] are not prominent enough. The performance of the said method is subject to the quality of image of the fake iris. In this sequence, He et al. [16] suggested another technique based on Wavelet Packet Transformation (WPT) that produces four sub images as output. In this tech-nique, the average computation time is much higher than the tradition scheme but it works efficiently in case of printed attack only [16].

The Local binary patterns (LBP) are implemented to analysis the texture of each sub regions of the input iris image [4]. The leaning methodology is implemented to deter-mine the LBP features that are not enough to detect artificial eye attack. This method is best suitable for contact lens attack.

Zhang et al. [5] implemented SIFT Descriptor to analyses the uniqueness in the local structure. The statistical features are extracted from the weighted LBP and ranking is assigned to each LBP encoding sequence. It is expensive in terms of time and also fails if extra texture on dirty optics. It also worked on small and poor datasets.

Galbally et  al. [17] obtained twenty-two quality features depending upon motion, focus, occlusion. There is no fixed criterion for selection of optimal sets of the features. The techniques were implemented and tested for printed lens images and best suits for high quality printed images. In continuous of this research, Galbally et al. [6] proposed LDA and QDA classifier separate the dataset into real and fake one but due to high com-plexity and relevant features it does practically feasible and relevant. This technique is suitable for photo attack only.

Chen et  al. [18] analyzed multispectral attribute of conjunctiva vessels and live irises. The 860-nm and 480-nm images have been used to extract the RNCV and ERIT successfully. It is expensive as extra hardware is needed but it fails if conjunctiva ves-sels are not visible.

Connel et  al. [19] provides the method for identifying the artificial items that con-fuses the real iris. The suggested system discovers the pupil of the eye and verified the result on one blue-eyed only but it needs detailed assessment. The multimodal biometric authentication system is most demanding and attracted many researchers to proposed robust and trusted system. Raghavendra & Busch [7] extracted Binary Statistical fea-tures from multi scale images of different dimension followed by SVM classification that uses eight independent extracted features.

Kohli et  al. [20] discovers Zernike minutes over the iris picture which encodes assortments in the structure of the iris picture. It misses the mark when different sorts of assaults exist actually circumstance. This methodology gives more underlines on auxil-iary highlights, not adequate for textural changes.

Hu et al. [8] use two models for regional highlights i.e. the spatial pyramid and rela-tional measure. Regional features are not robust in assessment to the low level features.

Bhogal et al. [21] determined six picture quality measures to separate genuine biom-etric attributes from information as utilized in introduction assaults for order of genuine and fashioned iris. The most ideal decision of picture quality is subject to the dataset. Various sorts of assault act distinctively so assault type assumes a significant job for characterization. In this way, the outcomes to inconspicuous assault are not reasonable.

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Fathy [22] proposed a strategy where entropy esteems are used. The entropy esteem is separated from the wavelet channels (WC) and neighbourhood twofold example (LBP) pictures of the channels. To separate among genuine and sham iris pictures these extricated highlights are utilized.

Agarwal et al. [11] proposed a feature descriptor for fake iris liveness detection. Circles restriction, non-iris districts recognition and condition at the time picture securing are the engaged region where scientists need to focus it. In ridiculing assault basic and textural highlights assume a significant job to separate among genuine and counterfeit example. Ideal and productive arrangement of the component vector is required to group between a genuine and phony one.

In this paper, we consider two sorts of assaults for example photograph assault and con-tact focal point assault. We have determined a novel set of feature vector from a new modi-fied feature descriptor i.e. EBHxEP which expose the solid connection between focus pixel and its hexa neighbor with less curse of dimensionality.

3 Proposed Descriptor

Liveness detection of iris is a complete classification problem, which is used to distinguish between genuine and forged iris images. The proposed framework is given in Fig. 3, having the following steps.

• Segmentation.• Normalization of the iris image.• Extraction of iris features using proposed descriptor.• Classification.

3.1 Segmentation

To separate the iris section from a given eye picture, with exact confinement of its limits with assessments of the middle directions and range of both the iris and the pupil. The student place is nasal to the iris community. Its sweep can go from 0.1 to 0.8 of the iris range. A very effective integro-differential operator [23] for determining all three param-eters defining the pupillary circle is as follows:

Obscuring at a scale σ, the above division technique fills in as a round edge locator. The info eye picture is checked for the most extreme fractional subsidiary for expanding span r0. The normalized iris image P(x, y) has center coordinates (x0, y0).∗ denotes the convolu-tion. Segmented iris and other segmented iris with some portion of eyelids and eyelashes as given in Fig. 4.

3.2 Normalization

A rubber sheet model [23] proposed by Daugman, to change the sectioned iris to a static rectangular district. This process is done by mapping the iris area, P(x, y), from the

(1)Max(r0, x0, y0)|||||G

�(r0) ∗

�

�r0 ∮r0,x0,y0

P(x, y)

2�r0ds|||||

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Cartesian directions (x, y) to the dimensionless polar directions (r, θ), and can be scien-tifically communicated as:

where r is in the unit span [0,1], and θ is an edge in the scope of [0, 2π]. After linear sam-pling process, segmented iris image is transformed into normalized iris image as repre-sented in Fig. 5.

(2)P[x(r, �), y(r, �)

]→ P(r, �)

Input: Iris Image

Iris Image:Segmented

Segmentation

Iris Image:Normalized

Normalization

Selection of Feature Vector

EBHxEP ProposedDescriptor

GenuineOR

Forged

Classifier

Fig. 3 Proposed Framework for Liveness Detection

Fig. 4 Human Eye IRIS Segmentation

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3.3 Enhanced Binary Hexagonal Extrema Pattern (EBHXEP)

An effective and improved image feature descriptor EBHxEP is proposed in this section. This descriptor utilized he relation between the center maxima pixel with its six hexagonal neigh-bors. Maxima and minima extrema local pattern are extracted from local hexagonal neighbors. Further, the relationship of these local binary hexagonal maxima and minima with the center pixel is used to train the EBHxEP descriptor. The proposed descriptor easily extracts the pro-ductive and ideal feature vector. These features must be sufficiently able to segregate between genuine iris and phony iris. The hexagonal sub section of an iris image is shown in Fig. 6.

The projected descriptor captures selective information at different angular widths 0°, 45°, 90°, 135°, 180°, 225°, 270° and 315° respectively with different radial widths R = 1 or R = 2. The extrema i.e. maxima and minima values and their indexes are extracted from an image. A relationship is established between the maxima center coordinates and it’s all neighbors at radial distance 1 or 2 as shown in Fig. 6.

Relationship of center pixel with its hexa neighbors is used to find the local pattern of the image. The connectivity relationship of the nearby hexagonal extrema is compared with the maxima center pixel. This is utilized to build up the association of center pixel with its hexa neighbors. From the maxima central pixel C, Pmax and Pmin are the local extrema intensity value with Imax and Imin positions. The pattern of EBHXEP for one local pattern with all 0’s and two 1’s is derived from Eq. 3.

(3)EBHxEPi,j = (EBHxEP

i,j

1,EBHxEP

i,j

2,…… , EBHxEP

i,j

Len)

Fig. 5 Linear Sampling Process [28]

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where EBHXEPi,jr is the rth element of EBHXEP

i,j and is calculated as follows and Len is the size of the EBHXEP

i,j pattern:

where Δ is the extrema relationship factor used to make the relationship of center pixel with its each of the six neighbors and given in Eq. 5 and Imax and Imin are the places of nearby maxima and minima.

where Diffmax = [Pmax∕R]−C and Diffmin = [Pmin∕R] −C are the difference between local extrema and central extrema C. The greatest size of will be founded on the estimation of value of minima and extrema relationship factor. When Imin = 6 and Δ = 2, then the maxi-mum possible value of k is 36, it means that the dimension len of the EBHXEP

i,j is 36. Slide a hexa window m × m over the entire image of 128 × 128 iris image with a translation step t = 3 . Finally, the computed EBHXEP over the image M having m rows and n columns (i.e. the dimension of M is m × n) is given as in Eq. 6.

The computation of EBHXEPi,j is illustrated in Fig. 7.

In this proposed descriptor, we consider one center pixel with maxima value and create a relationship with its hexa neighbors. In the state-of-the-art descriptor [11], author considers average of two center pixels, which has no real existence. The pro-posed descriptor calculates Diffmax and Diffmin with consideration of radial distance 1 or 2, which reflects the strong relationship with the maxima and minima local pixels. To

(4)EBHXEPi,jr=

{0 otherwise

1, if r = (Imax + 12Δ) or (Imin + 6 + 12Δ)

(5)Δ (Extrema Relationship Factor) =

⎧⎪⎨⎪⎩

0 If Diffmax ≤ 0 and Diffmin ≤ 0

1 If Diffmax > 0 and Diffmin > 0

2 else

(6)EBHXEP =1

(m − 2R)(n − 2R)

m−R∑i=R+1

n−R∑j=R+1

EBHXEPi,j

k

Fig. 6 Pattern of the Hexagonal sub section of an image

1

2 C1

3

4 C2 5

6

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP)…

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reduce the curse of dimensionality we have taken extrema relationship factor with the value 2, which gives the maximum dimension of 36 rather than 48 [11].

4 Experiments and Results

Support Vector Machine (SVM) is used to distinguish between real and fake iris images. This is two class classification problems. We pass the extricated feature set (preparing highlight set and testing highlight set) to the SVM classifier where it orders to the tried iris pictures into phony and live classification productively. SVM utilizes distinctive kernal mapping capacities, for example, direct, polynomial and Gaussian, which changes an ele-ment vector into another high dimensional space.

In the training stage both live and fake iris pictures are prepared by removing the high-lights vectors. In testing stage, input iris picture is taken care of and incorporate vector is enrolled. At the point when the last included vectors are figured from the two phases then SVM classifier bunches the class with respect to the data iris is certifiable or produced. Average Classification Accuracy (ACA) and Average Error Rate (AER) are two standard evaluation criteria on the presentation of a liveness framework and are calculated from Eq. 7 and 10. The proposed conspire is tried with condition of-workmanship techniques by utilizing these boundaries. The less estimation of AER and high estimation of ACA mir-rored the exhibition of the framework.

where

(7)Average Error Rate =(False Acceptance Rate + False Reject Rate)

2

(8)

False Acceptance Rate =No. of incorrectly accepts an imposter as the legitimate user

Total no. of imposters× 100

EBHXEP

i,j = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0}

Pmax

= 230, Imax

= 6, R= 1P

min= 10, I

min= 1, R= 2

Cmax

= 120

Diffmax

=[(Pmax

)/R]- C=110

Diffmin

= [(Pmin

)/R]-C = -115

Δ = 2I

max+12 Δ = 30

Imin

+6+ 12 Δ = 31

Fig. 7 Computation with an example

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and

where TPR defines the Trur Positive Rate and TNR defines the True Negative Rate.IIIT-D CLI [24] and ATVS-FIr DB [17] data set are used to test the efficiency of the

proposed descriptor. To evaluation the efficiency and accuracy of the proposed descriptor, we compared the results with different state-of-the-art methods.

4.1 IIIT‑D CLI Database

This dataset consists of 6570 iris images taken from 101 persons. Total 202 iris classes have been made by using left and right iris images of each person. Iris images have been captured with two iris devices:(1) the Cogent Dual and (2) the VistaFA2E.Sample images of the IIIT-D CLI database [24] is shown in Fig. 8. Structure for training and testing sam-ples is illustrated in Table 1.

The proposed strategy is contrasted and RDC [25], DAISY [26], LCPD [27] and LBHxEP [11]. These methods also consider the nearby neighboring data. Table  2 dem-onstrates the average error rate of various strategies on IIIT-D Cogent, Vista cosmetic lens. From investigation, it has been established that the proposed technique has lower

(9)False Rejection Rate =No. of the authorized user is wrongly rejected

Total no. of reals× 100

(10)ACA =TPR + TNR

2

(a)No Lens – Full (b) Soft Lens - Full (c) Textured Lens - Full

Fig. 8 Iris Image from IIIT-D CLI data set

Table 1 Construction of the IIIT-D CLI dataset

Sensor name No. of samples for training phase (Live/Spoof)

No. of samples in testing phase (Live/Spoof)

Cognet 180/180 550/550Vista 180/180 550/550

Table 2 AER Comparison

Dataset RDC [25] DAISY [26] LCPD [27] LBHXEP [11] EBHxEP

Cogent 5.5 17.2 11 4.8 3.9Vista 0.7 8.8 3.1 0.69 0.58

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AER when contrasted with other texture features based approaches [25–27] on IIIT-D CLI database.

For a ROC (Receiver Operating Characteristics) curve plotted, the abscissa and ordinate are False Positive Rate (FPR) and True Positive Rate (TPR) respectively. This is another compelling assessment technique utilized for performance analysis. Figure 9 outline ROC curve on IIIT-D dataset. Figure 9 unmistakably approving the powerful execution of the proposed descriptor to remove the ideal arrangement of neighborhood surface highlights, which are utilized to order for liveness recognition.

4.2 ATVS Database

In this dataset, the original images were captured from 50 different users. It comprises iris images of 50persons i.e. hundred different irises images. Using LG Iris Access EOU3000 camera, four samples of genuine and false iris were captured in two acquisition sessions. A total of one thousand six hundred images (800 genuine and 800forged) are available in this dataset. In training phase 350 iris images i.e. 175 genuine and 175forgedare used and in testing phase 900iris images are used in the ATVS dataset [17]. Figure 10 demonstrate sample images of genuine and phony iris picture from ATVS dataset.

For further validate the efficiency of the proposed descriptor, it is tested on ATVS data set. This data set is prepared by LG Iris Access EOU3000 for printed image attack. Struc-ture of the dataset for training and testing as depicted in Table 3. In the experiment 350 samples are used for training and 450 samples for testing.

The examination is performed of anticipated technique with cutting edge strategies [6, 11, 17, 25]. Figure 11 shows AER of various techniques on ATVS database. It has been established that the proposed descriptor has lower AER when contrasted with other static features put together techniques with respect to ATVS database.

0.88

0.9

0.92

0.94

0.96

0.98

1

0 0.05 0.1 0.15 0.2

Tru

e Po

sitiv

e R

ate

False Positive Rate

Receiver Operating Charactristics

LBP [4]

SIFT [5]

M-BSIF [7]

RDC [25]

LBHxEP[28]

EBHxEP (Proposed)

Fig. 9 ROC Curve for IIIT-D CLI data set

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In Fig.  12 the ACA is depicted and shows the effectiveness of the proposed descrip-tor. The Suggested descriptor classify between genuine and forged iris images with 99.1% accuracy.

ROC (Receiver Operating Characteristics) curve is plotted in Fig. 13 on ATVS-FIr data-set. Figure 13 approves the powerful execution of the proposed descriptor to remove the ideal arrangement of feature vector, which are utilized to classify for liveness detection. Comparison with state-of-art methods using different descriptor are also shown in this graph and well defined the performance of the proposed descriptor.

ATVS data base contains the iris images for printed attack and IIIT-D CLI data set contains the iris images for contact lens attack. To check the efficiency of proposed descriptor, we plot the ROC curve in Fig. 14, for ATVS and IIIT-D CLI data base and find that the suggested descriptor gives the better performance for contact lens attack instead of printed attack

5 Conclusion

In this letter we presented a proficient and viable feature descriptor for iris liveness detection. The connection between the center pixel and its hexa neighbor has been well described by suggested descriptor. The proposed descriptor technique considers six-neigh-bor pixels in hexagonal shape. This technique produces best result, as its higher symmetry, angular moments and precise goals. The suggested descriptor settles the “curse of dimen-sionality” issue. A binary SVM classifier is utilized for characterization utilizing straight mapping capacity. The proposed descriptor is tried on two standard data set i.e. ATVS and IIITD-CLI. The proposed framework accomplishes great precision for example 99.1% and low mistake rate 1.6 on ATVS dataset and normal error rate 3.9 and 0.58 on IIITD-CLI

Fig. 10 Images from ATVS database [17]

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Tabl

e 3

Con

struc

tion

of th

e A

TVS-

FIr D

B

Sens

orN

o. o

f sam

ples

for t

rain

ing

phas

e (L

ive/

Spoo

f)N

o. o

f sam

ples

in te

sting

pha

se (L

ive/

Spoo

f))

LG Ir

isA

cces

s EO

U30

0035

0/35

045

0/45

0

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0

0.5

1

1.5

2

2.5

3

3.5

QRF [16] IQM-QDA [6] RDC[25] LBHXEP [28] EBHxEP(Proposed)

AER (%)

Fig. 11 AER performance of public ATVS database

IQM-QDA[6]

IQM [20]ELBP [21] LBHxEP

[28] EBHxEP(Proposed)

97.8

85.895.6 98.3 99.1

Fig. 12 ACA performance of t in the public ATVS database

0.88

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0.92

0.94

0.96

0.98

1

0 0.05 0.1 0.15 0.2

Tru

e Po

sitiv

e R

ate

False Positive Rate

LBP [4]

SIFT [5]

M-BSIF [7]

RDC [25]

LBHxEP[28]

EBHxEP(Proposed)

Fig. 13 ATVS-FIr database:: ROC Curve

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dataset. The proposed feature descriptor shows lower AER and just as high precision for various sort of dataset. Finally, the outcomes verified the performance of suggested tech-nique under various types of the spoofing assault.

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Rohit Agarwal received the M. Tech. degree in Computer Science from UPTU Lucknow, India. He received the Ph.D. in the area of Computer Vision from GLA University Mathura, India. He has 19  years of teaching experience and currently, he is working as an Assistant Professor in Department of Computer Engineering and Applications, GLA University, Mathura, India. His research interests include Image Processing and Computer Vision.

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP)…

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Anand Singh Jalal received the M. Tech degree in Computer Science from Devi Ahilya Vishwavidyalaya, Indore, India. He received the PhD in the area of Computer Vision from Indian Institute of Informa-tion Technology (IIIT), Allahabad, India. He has 19 years of teaching and research experience and currently, he is working as a Professor in Department of Computer Engineering and Applications, GLA Univer-sity, Mathura, India. His research interests include Image Processing, Computer Vision and Pattern Recognition.

K. V. Arya achieved the Master of Engineering (ME) degree in Electri-cal Engineering from Indian Institute of Science (IISc), Bangalore, India and earned Ph. D. degree in Computer Science & Engineering from Indian Institute of Technology, Kanpur, India. He has more than 27 years teaching experience and published more than 150 journal and conference papers. He is senior member of IEEE, fellow of IETE, fel-low of Institution of Engineers (India), member of ACM and life mem-ber of IFTE. Currently, he is working as Professor in Department of Computer Science & Engineering, IET Lucknow, India. His research interests include Biometrics, Image Processing, Computer Vision and Reliability Analysis.