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For a robust face biometric system, a reliable antispoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. This paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance; especially HOOF descriptor yielding a near perfect half total error rate of 0% and 1.25% respectively.

Behavioral and neuropsychological studies suggest that upright and inverted face stimuli are processed by computationally and anatomically distinct systems. Specifically, inverted faces seem to be addressed by general object perception systems, avoiding face-specific processes. We tested this model by examining the fMRI signal response of a functionally defined fusiform face area and bilateral object-responsive cortical areas during the perception of upright and inverted stimuli (faces and cars). While inversion of face stimuli had no effect upon the magnitude of responses in the fusiform face area, inverted faces evoked greater neural responses compared to upright faces within object regions. This finding supports the assertion that object areas are involved to a greater degree in the perception of inverted vs upright faces.

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This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.