Reza Derakhshani

Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners

Fingerprints are the oldest and most widely used biometrics for personal identification. Unfortunately, it is usually possible to deceive automatic fingerprint identification systems by presenting a well-duplicated synthetic or dismembered finger. This paper introduces one method to provide fingerprint vitality authentication in order to solve this problem. Detection of a perspiration pattern over the fingertip skin identifies the vitality of a fingerprint. Mapping the two-dimensional fingerprint images into one-dimensional signals, two ensembles of measures, namely static and dynamic measures, are derived for classification. Static patterns as well as temporal changes in dielectric mosaic structure of the skin, caused by perspiration, demonstrate themselves in these signals. Using these measures, this algorithm quantifies the sweating pattern and makes a final decision about vitality of the fingerprint by a neural network trained by examples.

Time-series detection of perspiration as a liveness test in fingerprint devices

Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets.

Enhancement and Registration Schemes for Matching Conjunctival Vasculature

Ocular biometrics has made significant strides over the past decade primarily due to the rapid advances in iris recognition. Recent literature has investigated the possibility of using conjunctival vasculature as an added ocular biometric. These patterns, observed on the sclera of the human eye, are especially significant when the iris is off-angle with respect to the acquisition device resulting in the exposure of the scleral surface. In this work, we design enhancement and registration methods to process and match conjunctival vasculature obtained under non-ideal conditions. The goal is to determine if conjunctival vasculature is a viable biometric in an operational environment. Initial results are promising and suggest the need for designing advanced image processing and registration schemes for furthering the utility of this novel biometric. However, we postulate that in an operational environment, conjunctival vasculature has to be used with the iris in a bimodal configuration.

A Texture-Based Neural Network Classifier for Biometric Identification using Ocular Surface Vasculature

In an earlier work we had explored the possibility of utilizing the vascular pattern of the sclera, episclera, and conjunctiva as a biometric indicator. These blood vessels, which can be observed on the white part of the human eye, demonstrate rich and seemingly unique details in visible light, and can be easily imaged using commercially available digital cameras. In this work we discuss a new method to represent and match the textural intricacies of this vascular structure using wavelet-derived features in conjunction with neural network classifiers. Our experimental results, based on the evidence of 50 subjects, indicate the potential of the proposed scheme to characterize the individuality of the ocular surface vascular patterns and further confirm our assertion that these patterns are indeed unique across individuals.

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.

Biometric recognition of conjunctival vasculature using GLCM features

Besides the iris, conjunctival vasculature may also be used for ocular biometric recognition. Conjunctival vessel patterns can be easily observed in the visible spectrum and can compensate for off-angle or otherwise occluded iridial texture. In this paper, classification of conjunctival vasculature using Gray Level Co-occurrence Matrix (GLCM) is studied. Statistical features of GLCM, i.e., contrast, correlation, energy and homogeneity, were used in conjunction with Fisher linear discriminant analysis and regularized neural network classifiers in order to recognize textures arising from conjunctival vessels. Match score level fusion of Fisher LDA and neural networks provided the best results, resulting in a test set equal error rate (EER) and area under receiver operating characteristics curve (ROC AUC) of 13.97% and 0.9333, respectively. These figures improved to 11.9% and 0.9504 after fusion of LDA and neural network match scores.

On classifiability of wavelet features for EEG-based brain-computer interfaces

Given their multiresolution temporal and spectral locality, wavelets are powerful candidates for decomposition, feature extraction, and classification of non-stationary electroencephalographic (EEG) signals for brain-computer interface (BCI) applications. Wavelet feature extraction methods offer several options through the choice of wavelet families and decomposition architectures. The classification results of EEG signals generated from imagined motor, cognitive, and affective tasks are presented using support vector machine (SVM) classifiers, indicating that these methods are suitable for imagined motor, cognitive and affective classification. Classifier performances of better than 80% for six imagined motor tasks, and for two affective tasks were achieved. Three cognitive tasks were successfully classified with 70% accuracy. The methods can be used with a variety of EEG signal reference methods and electrode placement locations. Wavelet features performed satisfactorily in the presence of noise when the classifiers were presented with contaminated training data.

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 Comparison of EEG Preprocessing Methods using Time Delay Neural Networks

Multichannel recordings of EEG data during various mental tasks are processed using two popular methods, independent component analysis (ICA) and matching pursuit (MP). The results are fed to a time delay neural network (TDNN) for classification of each mental task. Based on the results of the test sets, we analyzed the effectiveness of ICA and MP methods for use in EEG preprocessing and TDNN classification. It is shown that ICA is more effective than MP in lowering the neural network classification error; however this advantage is not significant

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.