Iris and Periocular
Recognition
Using Mobile Phones
Work done with Asem Othman
Arun Ross
Associate Professor
Michigan State University
rossarun@cse.msu.edu
http://www.cse.msu.edu/~rossarun
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Biometrics in Mobile Phones
 Here, the term “mobile” is referred to modern
phones, tablets and similar smart devices
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Ocular Biometrics in a Smartphone
 Performing ocular recognition in a smartphone
environment
 Ocular images acquired using a smartphone camera
 Texture of dark-colored irises not easily resolved in
color images
 Images acquired in various types of lighting
conditions
 Resolution and view-point of images can change
 Images can be blurred or occluded with glasses
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Work Elsewhere
 Smart Sensors Limited has
ported their iris SDK into the
Android OS
AOPTIX STRATUS MX
 Some smartphone devices use
NIR accessories to perform iris
recognition
 AOptix Stratus MX is an iPhone
hardware peripheral equipped
with an iris sensor
 EyeVerify uses scleral patterns
to authenticate a mobile phone
user
EYE VERIFY
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MICHE Database
 Color images acquired using iPhone5, Galaxy S4, Tablet 2
Iris
Periocular
iPhone 5
Samsung Galaxy
S4
Samsung Galaxy Tablet
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De Marsico et al, “FIRME: Face and Iris Recognition for Mobile Engagement,” IMAVIS 2014
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Images from iPhone 5
###= subject number, IN = indoor, OU = outdoor, F =Front
cam, R = Rear Cam, RI = right eye
001_IP5_IN_R_RI_01_1
001_IP5_IN_F_RI_01_3
001_IP5_OU_R_RI_01_1
001_IP5_OU_F_RI_01_1
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Sensor Positioning
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Illumination
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Other Challenges
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Periocular Biometrics
 Periocular recognition focuses on the region in the
vicinity of the eye
This is on a face database
PERIOCULAR
FACE
Partial face image
Periocular region segmentation
for partial faces
Park, Jillela, Ross, Jain “Periocular Biometrics in the Visible Spectrum,” TIFS 2011
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Block Diagram of Analysis
 Methods to enhance images
 Methods to extract robust features from images
 Different distance measures for matching
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Illumination Normalization
 Multi Scale Retinex (MSR)

N
RMSR   n {log I ( x, y)  log[ Fn ( x, y ) * I ( x, y)]}
n 1
 Multi Scale Self Quotient (MSQ)

N
RMSQ   nT ( I ( x, y ) /( Fn ( x, y) * I ( x, y)))
n 1
 Homomorphic Normalization (HOM)
f(x,y)
ln
DFT
H(u,v)
(DFT)-1
exp
 Wavelet-based Normalization (WA)
 Apply histogram equalization to detailed wavelet
coefficients
g(x,y)
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Feature Extraction
 Segment and align the periocular region
 Tessellate image into overlapping patches
 Apply texture descriptor to each patch
 Compute histogram of texture descriptors for
each patch
 Concatenate all histograms to obtain a fixedlength feature vector
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Alignment
 Alignment based on eye corners
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Texture Descriptors Used
 Local Binary Pattern (LBP)
 Local Phase Quantization (LPQ)
 Binarized Statistical Image Features (BSIF)
 Leung-Malik Filter (LMF)
 Histogram of Oriented Gradients (HOG)
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Leung-Malik (LM) Filter Bank
 The LM filter bank has a mix of edge, bar and
spot filters at multiple scales and orientations
T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using
three-dimensional textons,” IJCV 2001
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Distance Metrics Used
 Euclidean distance (EUC)
 Chi-squared distance (CHI-SQ)
 Cosine measure (COS)
 Earth Mover's Distance (EMD)
 Histogram Intersection distance (HI)
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Experiment Results
 Metric: Verification rate at 1% FAR
BSI
LMF
LPQ
F
51
%
55
%
73
%
50%
65%
80%
61
%
70
%
75
%
HO
G
53%
65%
75%
Before Alignment (HOM)
After Alignment (MSR)
Without Glasses and In-Focus (MSR)
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Summary
 Iris biometric did not perform well on this
database
 Ocular biometric was more competitive
 Best performance:
 Multi Scale Retinex (MSR) in conjunction with
HOG, BSIF, or LPQ
 Using only in-focus images without glasses
further improves performance
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Future Work
 Accurate alignment
 Quality-based image enhancement
 Subspace analysis of texture descriptors
 Cross-camera evaluation
 Porting code into smartphone platform
Iris and Periocular
Recognition
Using Mobile Phones
Work done with Asem Othman
Arun Ross
Associate Professor
Michigan State University
rossarun@cse.msu.edu
http://www.cse.msu.edu/~rossarun