Access control and surveillance applications like walking-through security gates and immigration control points have a great demand for convenient and accurate biometric recognition in unconstrained scenarios with low user cooperation. The periocular region, which is a relatively new biometric trait, has been attracting much attention for recognition of an individual in such scenarios. This paper proposes a periocular recognition method that combines Phase-Based Correspondence Matching (PB-CM) with a texture enhancement technique. PB-CM has demonstrated high recognition performance in other biometric traits, e.g., face, palmprint and finger-knuckle-print. However, a major limitation for periocular region is that the performance of PB-CM degrades when the periocular skin has poor texture. We address this problem by applying texture enhancement and found out that variance normalization of texture significantly improves the performance of periocular recognition using PB-CM. Experimental evaluation using three public databases demonstrates the advantage of the proposed method compared with conventional methods.

We propose an iterative frequency estimation method for accuracy improvement of discrete Fourier transform (DFT) phase-based methods. It iterates frequency estimation and phase calculation based on the DFT phase-based methods, which maximizes the signal-to-noise floor ratio at the frequency estimation position. We apply it to three methods, the phase difference estimation, the derivative estimation, and the arctan estimation, which are known to be among the best DFT phase-based methods. Experimental results show that the proposed method shows meaningful reductions of the frequency estimation error compared to the conventional methods especially at low signal-to-noise ratio.

This paper presents a high-accuracy 3D (three-dimen-sional) measurement system using multi-camera passive stereo vision to reconstruct 3D surfaces of free form objects. The proposed system is based on an efficient stereo correspondence technique, which consists of (i) coarse-to-fine correspondence search, and (ii) outlier detection and correction, both employing phase-based image matching. The proposed sub-pixel correspondence search technique contributes to dense reconstruction of arbitrary-shaped 3D surfaces with high accuracy. The outlier detection and correction technique contributes to high reliability of reconstructed 3D points. Through a set of experiments, we show that the proposed system measures 3D surfaces of objects with sub-mm accuracy. Also, we demonstrate high-quality dense 3D reconstruction of a human face as a typical example of free form objects. The result suggests a potential possibility of our approach to be used in many computer vision applications.

This paper presents a technique for disparity selection in the context of binocular pursuit. For vergence control in binocular pursuit, it is a crucial problem to find the disparity which corresponds to the target among multiple disparities generally observed in a scene. To solve the problem of the selection, we propose an approach based on histogramming the disparities obtained in the scene. Here we use an extended phase-based disparity estimation algorithm. The idea is to slice the scene using the disparity histogram so that only the target remains. The slice is chosen around a peak in the histogram using prediction of the target disparity and target location obtained by back projection. The tracking of the peak enables robustness against other, possibly dominant, objects in the scene. The approach is investigated through experiments and shown to work appropriately.