Gholamreza Amayeh

Peg-Free Hand Shape Verification Using High Order Zernike Moments

Hand-based verification is a key biometric technology with a wide range of potential applications both in industry and government. The focus of this work is on improving the efficiency, accuracy, and robustness of hand-based verification. In particular, we propose using high-order Zernike moments to represent hand geometry, avoiding the more difficult and prone to errors process of hand-landmark extraction (e.g., finding finger joints). The proposed system operates on 2D hand silhouette images acquired by placing the hand on a planar lighting table without any guidance pegs, increasing the ease of use compared to conventional systems. Zernike moments are powerful translation, rotation, and scale invariant shape descriptors. To deal with several practical issues related to the computation of highorder Zernike moments including computational cost and lack of accuracy due to numerical errors, we have employed an efficient algorithm that uses arbitrary precision arithmetic, a look-up table, and avoids recomputing the same terms multiple times [2]. The proposed hand-based authentication system has been tested on a database of 40 subjects illustrating promising results. Qualitative comparisons with state of the art systems illustrate comparable of better performance.

Accurate and efficient computation of high order zernike moments

Zernike Moments are useful tools in pattern recognition and image analysis due to their orthogonality and rotation invariance property. However, direct computation of these moments is very expensive, limiting their use especially at high orders. There have been some efforts to reduce the computational cost by employing quantized polar coordinate systems, which also reduce the accuracy of the moments. In this paper, we propose an efficient algorithm to accurately calculate Zernike moments at high orders. To preserve accuracy, we do not use any form of coordinate transformation and employ arbitrary precision arithmetic. The computational complexity is reduced by detecting the common terms in Zernike moments with different order and repetition. Experimental results show that our method is more accurate than other methods and it has comparable computational complexity especially in case of using large images and high order moments.

Gender classification from hand shape

Many social interactions and services today depend on gender. In this paper, we investigate the problem of gender classification from hand shape. Our work has been motivated by studies in anthropometry and psychology suggesting that it is possible to distinguish between male and female hands by considering certain geometric features. Our system segments the hand silhouette into six different parts corresponding to the palm and fingers. To represent the geometry of each part, we use region and boundary features based on Zernike moments and Fourier descriptors. For classification, we compute the distance of a given part from two different eigenspaces, one corresponding to the male class and the other corresponding to female class. We have experimented using each part of the hand separately as well as fusing information from different parts of the hand. Using a small database containing 20 males and 20 females, we report classification results close to 98% using score-level fusion and LDA.

Hand-based verification and identification using palm-finger segmentation and fusion

Hand-based verification/identification represent a key biometric technology with a wide range of potential applications both in industry and government. Traditionally, hand-based verification and identification systems exploit information from the whole hand for authentication or recognition purposes. To account for hand and finger motion, guidance pegs are used to fix the position and orientation of the hand. In this paper, we propose a component-based approach to hand-based verification and identification which improves both accuracy and robustness as well as ease of use due to avoiding pegs. Our approach accounts for hand and finger motion by decomposing the hand silhouette in different regions corresponding to the back of the palm and the fingers. To improve accuracy and robustness, verification/recognition is performed by fusing information from different parts of the hand. The proposed approach operates on 2D images acquired by placing the hand on a flat lighting table and does not require using guidance pegs or extracting any landmark points on the hand. To decompose the silhouette of the hand in different regions, we have devised a robust methodology based on an iterative morphological filtering scheme. To capture the geometry of the back of the palm and the fingers, we employ region descriptors based on high-order Zernike moments which are computed using an efficient methodology. The proposed approach has been evaluated both for verification and recognition purposes on a database of 101 subjects with 10 images per subject, illustrating high accuracy and robustness. Comparisons with related approaches involving the use of the whole hand or different parts of the hand illustrate the superiority of the proposed approach. Qualitative and quantitative comparisons with state-of-the-art approaches indicate that the proposed approach has comparable or better accuracy.

Accurate and Efficient Computation of Gabor Features in Real-Time Applications

Gabor features are widely used in many computer vision applications such as image segmentation and pattern recognition. To extract Gabor features, a set of Gabor filters tuned to several different frequencies and orientations is utilized. The computational complexity of these features, due to their non-orthogonality, prevents their use in many real-time or near real-time tasks. Many research efforts have been made to address the computational complexity of Gabor filters. Most of these techniques utilize the separability of Gabor filters by decomposing them into 1-D Gaussian filter. The main issue in these techniques is the efficient pixel interpolation along the desired direction. Sophisticated interpolation mechanisms minimize the interpolation error with the increased computational complicity. This paper presents a novel framework in computation of Gabor features by utilizing a sophisticated interpolation scheme --- quadratic spline --- without increasing the overall computational complexity of the process. The main contribution of this work is the process of performing the interpolation and the convolution in a single operation. The proposed approach has been used successfully in real-time extraction of Gabor features from video sequence. The experimental results show that the proposed framework improves the accuracy of the Gabor features while reduces the computational complexity.

A Component-Based Approach to Hand Verification

This paper describes a novel hand-based verification system based on palm-finger segmentation and fusion. The proposed system operates on 2D hand images acquired by placing the hand on a planar lighting table without any guidance pegs. The segmentation of the palm and the fingers is performed without requiring the extraction of any landmark points on the hand. First, the hand is segmented from the forearm using a robust, iterative methodology based on morphological operators. Then, the hand is segmented into six regions corresponding to the palm and the fingers using morphological operators again. The geometry of each component of the hand is represented using high order Zernike moments which are computed using an efficient methodology. Finally, verification is performed by fusing information from different parts of the hand. The proposed system has been evaluated on a database of 101 subjects illustrating high accuracy and robustness. Comparisons with competitive approaches that use the whole hand illustrate the superiority of the proposed, component-based, approach both in terms of accuracy and robustness. Qualitative comparisons with state of the art systems illustrate that the proposed system has comparable or better performance.

Improvement of Zernike moment descriptors on affine transformed shapes

In general, Zernike moments are often used efficiently as shape descriptors of image objects, such as logos or trademarks that cannot be defined by a single contour. However, because these moments are defined in a unit disk space and extracted by a polar raster sampling shape, information of skewed and stretched shapes is lost. As a result, they can be inefficient shape descriptors when there is skew and stretch distortion. In this paper, a method is proposed that addresses this issue. More specifically, Zernike moments are obtained from a transformed unit disk space that allows for the extraction of shape descriptors which are invariant to rotation, translation, and scale as well as skew and stretch, thus preserving more shape information for the feature extraction process. The experimental results demonstrate that the proposed algorithm is more accurate in relation to skew and stretch distortions when compared to other available schemes reported in the literature.

A new approach to hand-based authentication

Hand-based authentication is a key biometric technology with a wide range of potential applications both in industry and government. Traditionally, hand-based authentication is performed by extracting information from the whole hand. To account for hand and finger motion, guidance pegs are employed to fix the position and orientation of the hand. In this paper, we consider a component-based approach to hand-based verification. Our objective is to investigate the discrimination power of different parts of the hand in order to develop a simpler, faster, and possibly more accurate and robust verification system. Specifically, we propose a new approach which decomposes the hand in different regions, corresponding to the fingers and the back of the palm, and performs verification using information from certain parts of the hand only. Our approach operates on 2D images acquired by placing the hand on a flat lighting table. Using a part-based representation of the hand allows the system to compensate for hand and finger motion without using any guidance pegs. To decompose the hand in different regions, we use a robust methodology based on morphological operators which does not require detecting any landmark points on the hand. To capture the geometry of the back of the palm and the fingers in suffcient detail, we employ high-order Zernike moments which are computed using an effcient methodology. The proposed approach has been evaluated on a database of 100 subjects with 10 images per subject, illustrating promising performance. Comparisons with related approaches using the whole hand for verification illustrate the superiority of the proposed approach. Moreover, qualitative comparisons with state-of-the-art approaches indicate that the proposed approach has comparable or better performance.

A Comparative Study of Hand Recognition Systems

Hand-based recognition represents a key biometric technology with a wide range of potential applications both in industry and government. By far, many different handbased recognition algorithms have been developed. This paper presents a comparative study to evaluate the performance of three state of the art hand-based recognition methods. Using the University of Nevada at Reno (UNR) and the University of Notre Dame (UND) hand databases, we compare a geometricbased method, a component-based approach using Zernike moments, and an algorithm employing 3D finger surface features. Both recognition and authentication experiments have been conducted to investigate the performance and robustness of the three methods. Our experimental results show that Zernike descriptors yield features that are more robust and accurate compared to hand geometric features and 3D finger surface features.

Fingerprint Images Enhancement in Curvelet Domain

Due to variety of fingerprint images in quality, it is essential to perform a fingerprint enhancement stage before extracting minutiae. Since the performance of an automatic fingerprint authentication system depends on accuracy of extracted features, designing an efficient and accurate enhancement module is critical. In this paper we propose a new fingerprint enhancement method based on Gabor filter in Curvelet domain which can improve the clarity and continuity of ridge and valley structures. In proposed method first we apply Fast Discrete Curvelet Transform (FDCT) on query image. Then Gabor filter is employed on the coarse scale coefficients and a soft thresholding function is applied on the fine scale coefficients. Finally we reconstruct fingerprint image using those modified coefficients. Our primary experimental results on a small test set, which includes 21 fingerprint images, show the promising performance compare to Gabor-based and Wavelet-based methods.