Sashi K. Saripalle

Fusing iris and conjunctival vasculature: Ocular biometrics in the visible spectrum

Ocular biometrics refers to the imaging and use of characteristic features of the eyes for personal identification. Traditionally, the iris has been viewed as a powerful ocular biometric cue. However, the iris is typically imaged in the near infrared (NIR) spectrum. RGB images of the iris, acquired in the visible spectrum, offer limited biometric information for dark-colored irides. In this work, we explore the possibility of performing ocular biometric recognition in the visible spectrum by utilizing the iris in conjunction with the vasculature observed in the white of the eye. We design a weighted fusion scheme to combine the information originating from these two modalities. Experiments on a dataset of 50 subjects indicate that such a fusion scheme improves the equal error rate by a margin of 4.5% over an iris-only approach.

Classification of body movements based on posturographic data

The human body, standing on two feet, produces a continuous sway pattern. Intended movements, sensory cues, emotional states, and illnesses can all lead to subtle changes in sway appearing as alterations in ground reaction forces and the bodys center of pressure (COP). The purpose of this study is to demonstrate that carefully selected COP parameters and classification methods can differentiate among specific body movements while standing, providing new prospects in camera-free motion identification. Force platform data were collected from participants performing 11 choreographed postural and gestural movements. Twenty-three different displacement- and frequency-based features were extracted from COP time series, and supplied to classification-guided feature extraction modules. For identification of movement type, several linear and nonlinear classifiers were explored; including linear discriminants, nearest neighbor classifiers, and support vector machines. The average classification rates on previously unseen test sets ranged from 67% to 100%. Within the context of this experiment, no single method was able to uniformly outperform the others for all movement types, and therefore a set of movement-specific features and classifiers is recommended.

A video-based hyper-focal imaging method for iris recognition in the visible spectrum

We design and implement a hyper-focal imaging system for acquiring iris images in the visible spectrum. The proposed system uses a DSLR Canon T2i camera and an Okii controller to capture videos of the ocular region at multiple focal lengths. The ensuing frames are fused in order to yield a single image with higher fidelity. Further, the proposed setup extends the imaging depth-of-field (DOF), thereby preempting the need for employing expensive cameras for increased DOF. Experiments convey the benefits of utilizing a hyper-focal system over a traditional fixed-focus system for performing iris recognition in visible spectrum.

ICIP 2016 competition on mobile ocular biometric recognition

With the unprecedented mobile technology revolution, a number of ocular biometric based personal recognition schemes have been proposed for mobile use cases. The aim of this competition is to evaluate and compare the performance of mobile ocular biometric recognition schemes in visible light on a large scale database (VISOB Dataset ICIP2016 Challenge Version) using standard evaluation methods. Four different teams from universities across the world participated in this competition, submitting five algorithms altogether. The submitted algorithms applied different texture analysis in a learning or a non-learning based framework for ocular recognition. The best results were obtained by a team from Norwegian Biometrics Laboratory (NTNU, Norway), achieving an Equal Error Rate of 0.06% over a quarantined test set.

Computational methods for objective assessment of conjunctival vascularity

Assessment of vascularity of conjunctival has many diagnostic and prognostic applications, thus creation of computational methods for its fast and objective assessment is of importance. Here we provide two different methods for estimation of conjunctivas vascularity from color digital images, with our best results showing a correlation coefficient of 0.89 between the predicted and ground truth values using a committee of artificial neural networks.

Post-mortem iris biometric analysis in Sus scrofa domesticus

Although biometric utility of ante-mortem human iris tissue has been long established, post-mortem study of human iris tissue for its biometric utility has only been speculated. Given obstacles in measuring and analyzing biometric capability of post-mortem human iris tissue, an investigation into the feasibility of using post-mortem Sus scrofa domesticus iris tissue as a biometric is undertaken. The contributions of our work are two-fold: first, our method discusses a feasible alternative to human iris for study of post-mortem iris biometric analysis. Second, we report the performance of iris biometrics over a period of time after death. Previous studies have only reported qualitative changes in iris after death while for the first time we measure the biometric capacity of post-mortem iris tissue.

Enhanced obfuscation for multi-part biometric templates

Biometrie authentication is being exceedingly utilized into mobile devices as an alternative to passwords. However, for security and privacy reasons, it is important to protect the biometric template. In this paper, we introduce a method to obfuscate and match certain biometric templates comprised of multiple local descriptors derived around spatial interest points. Obfuscation starts by insertion of chaff (fake) interest points along with their respective synthesized descriptors that are statistically similar to original descriptors. Fusion of local matches along with global outlier rejection is used to mitigate the impact of the resulting obfuscation on biometric matching accuracy. The efficacy of the proposed method is demonstrated through ocular and face recognition experiments.

Machine learning methods for credibility assessment of interviewees based on posturographic data.

This paper discusses the advantages of using posturographic signals from force plates for non-invasive credibility assessment. The contributions of our work are two fold: first, the proposed method is highly efficient and non invasive. Second, feasibility for creating an autonomous credibility assessment system using machine-learning algorithms is studied. This study employs an interview paradigm that includes subjects responding with truthful and deceptive intent while their center of pressure (COP) signal is being recorded. Classification models utilizing sets of COP features for deceptive responses are derived and best accuracy of 93.5% for test interval is reported.