Peg-Free Hand Shape Verification Using High Order Zernike Moments

Abstract

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.

Authentication systems: principles and threats

“…This authentication takes into consideration the shape and geometrical details of the whole hand (Amayeh, Bebis, Erol, & Nicolescu, 2006). Length and width of the fingers, the diameter of the palm and the perimeter are examples of these geometric features.…”

Hand-based multimodal identification system with secure biometric template storage

“…The hand's geometry is not known to be very distinctive between individuals [5] and the issue of its uniqueness is still somewhat controversial [6]. Recent research studies [7,8,9] have shown high recognition rates, but the datasets used in these studies were acquired in highly controlled environments. Thus, even if the hand's geometry is unique amongst a large population, it might not be feasible to extract accurate features in a rather unconstrained environment [6].…”

Digital Filter Implementation of Orthogonal Moments

“…Two, many recent works have involved increasingly higher moment orders. They include, among others, invariant image watermarking (30 orders) [24], moving object reconstruction (55 orders) [25], and hand shape verification (60 orders) [26].…”