title: Embedded palmprint recognition system using OMAP 3530.
abstract: We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the central pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.
authors.affiliation: Shenzhen Key Laboratory of Embedded System Design, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. llshen@szu.edu.cn
authors.forename: Linlin
authors.initials: L
authors.lastname: Shen
authors.forename: Shipei
authors.initials: S
authors.lastname: Wu
authors.forename: Songhao
authors.initials: S
authors.lastname: Zheng
authors.forename: Zhen
authors.initials: Z
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000295:instrumentation / Q000379:methods*; D003878:Dermatoglyphics; D004867:Equipment Design; D006225:Hand / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software