Koji Kobayashi

An Effective Approach for Iris Recognition Using Phase-Based Image Matching

This paper presents an efficient algorithm for iris recognition using phase-based image matching - an image matching technique using phase components in 2D discrete Fourier transforms (DFTs) of given images. Experimental evaluation using the CASIA iris image databases (versions 1.0 and 2.0) and Iris challenge evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes it possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses the major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier phase code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art digital signal processing (DSP) technology.

An efficient iris recognition algorithm using phase-based image matching

A major approach for iris recognition today is to generate feature vectors corresponding to individual iris images and to perform iris matching based on some distance metrics. One of the difficult problems in feature-based iris recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents an efficient algorithm for iris recognition using phase-based image matching. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of iris images makes possible to achieve highly robust iris recognition in a unified fashion with a simple matching algorithm. Experimental evaluation using an iris image database clearly demonstrates an efficient matching performance of the proposed algorithm.

A fingerprint recognition algorithm using phase-based image matching for low-quality fingerprints

A major approach for fingerprint recognition today is to extract minutiae from fingerprint images and to perform fingerprint matching based on the number of corresponding minutiae pairings. One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface condition, which may vary depending on environmental or personal causes. Addressing this problem, this paper presents a fingerprint recognition algorithm using phase-based image matching. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of fingerprint images makes possible to achieve highly robust fingerprint recognition for low-quality fingerprints. Experimental evaluation using a set of fingerprint images captured from fingertips with difficult conditions (e.g., dry fingertips, rough fingertips, allergic-skin fingertips) demonstrates an efficient recognition performance of the proposed algorithm compared with a typical minutiae-based algorithm.

3D face recognition using passive stereo vision

This paper proposes a face recognition system that uses (i) passive stereo vision to capture three-dimensional (3D) facial information and (ii) 3D matching using a simple ICP (iterative closest point) algorithm. So far, the reported 3D face recognition techniques assume the use of active 3D measurement for 3D facial capture. However, active methods employ structured illumination (structure projection, phase shift, gray-code demodulation, etc.) or laser scanning, which is not desirable in many human recognition applications. A major problem of using passive stereo vision for 3D measurement is its low accuracy, and thus no passive methods for 3D face recognition have been reported previously. Addressing this problem, we have newly developed a high-accuracy 3D measurement system based on passive stereo vision, where phase-based image matching is employed for sub-pixel disparity estimation. This paper presents the first attempt to create a practical face recognition system based on fully passive 3D reconstruction.

A Palmprint Recognition Algorithm Using Phase-Only Correlation

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

A Palmprint Recognition Algorithm using Phase-Based Image Matching

A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics. One of the difficult problems in feature-based recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents a palmprint recognition algorithm using phase-based image matching. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes possible to achieve highly robust palmprint recognition. Experimental evaluation using a palmprint image database clearly demonstrates an efficient matching performance of the proposed algorithm.

A Subpixel Image Matching Technique Using Phase-Only Correlation

This paper presents a high-accuracy image matching technique using a phase-only correlation (POC) function. The POC-based image matching enables estimation of image displacements with 1/10~1/100-pixel accuracy by a function fitting technique using the closed-form representation of the POC functions peak. This method requires an iterative process for the nonlinear function fitting, resulting in long computation times. In this paper, we propose a peak evaluation formula (PEF) that directly estimates the correlation peak location from actual 2-D data array of the POC function. Experimental evaluation shows that the proposed method reduces computation time without sacrificing image matching accuracy

An Iris Recognition System Using Phase-Based Image Matching

This paper presents an implementation of iris recognition algorithm using phase-based image matching-an image matching technique using phase components in 2D discrete Fourier transforms (DFTs) of given images. Our experimental observation clearly shows that the use of phase components of iris images makes possible to achieve highly accurate iris recognition even for low-quality iris images. In this paper, we consider the problem of designing a compact phase-based iris recognition algorithm especially suitable for hardware implementation. We also present prototype implementation of an iris recognition system based on the proposed algorithm. The prototype system fully utilizes state-of-the-art DSP (digital signal processor) technology to achieve real-time iris recognition capability within a compact hardware module.

A Sub-Pixel Stereo Correspondence Technique Based on 1D Phase-only Correlation

This paper presents a technique for high-accuracy correspondence search between two rectified images using 1D phase-only correlation (POC). The correspondence search between stereo images can be reduced to 1D search through image rectification. However, we usually employ block matching with 2D rectangular image blocks for finding the best matching point in the 1D search. We propose the use of 1D POC (instead of 2D block matching) for stereo correspondence search. The use of 1D POC makes possible significant reduction in computational cost without sacrificing reconstruction accuracy compared with the 2D POC-based approach. Also, the resulting reconstruction accuracy is much higher than those of conventional stereo matching techniques using SAD (sum of absolute differences) and SSD (sum of squared differences) combined with sub-pixel disparity estimation.

A phase-based iris recognition algorithm

This paper presents an efficient algorithm for iris recognition using phase-based image matching. The use of phase components in two-dimensional discrete Fourier transforms of iris images makes possible to achieve highly robust iris recognition with a simple matching algorithm. Experimental evaluation using the CASIA iris image database (ver. 1.0 and ver. 2.0) clearly demonstrates an efficient performance of the proposed algorithm.