On Hierarchical Palmprint Coding with Multi-Features for Personal Identification in Large Databases.
Jane You, Adams Wai‐Kin Kong, David D. Zhang, King Hong Cheung · 2003
Abstract—Automatic personal identification is a significant component of security systems with many challenges and practical applications. The advances in biometric technology have led to the very rapid growth in identity authentication. This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement, and fast search for the best match, we propose a hierarchical multifeature coding scheme to facilitate coarse-to-fine matching for efficient and effective palm-print verification and identification in a large database. In our approach, four-level features are defined: global geometry-based key point distance (Level-1 feature), global texture energy (Level-2 feature), fuzzy “interest ” line (Level-3 feature), and local direc-tional texture energy (Level-4 feature). In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by a coarse-to-fine guided search. The proposed method has been tested in a database with 7752 palmprint images from 386 different palms. The use of Level-1, Level-2, and Level-3 features can remove candidates from the database by 9.6%, 7.8%, and 60.6%, respectively. For a system embedded with an Intel Pentium III processor (500 MHz), the execution time of the simulation of our hierarchical coding scheme for a large database with 106 palmprint samples is 2.8 s while the traditional sequential approach requires 6.7 s with 4.5 % verification equal error rate. Our experimental results demonstrate the feasibility and effectiveness of the proposed method. Index Terms—Biometric identification, feature extraction and representation, fuzzy set, guided search, palmprint classification, texture measurement. I.