On Hierarchical Palmprint Coding With Multiple Features for Personal Identification in Large Databases

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 palmprint 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 directional 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 10/sup 6/ 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.