Abstract
Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users' satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.
Highlights
Hand-based multibiometric recognition system occupies an important position in the field of biometric recognition
Multifeature fusion undoubtedly increases the computational complexity, feature dimension, and computation time. erefore, the bimodal feature recognition system proposed in this paper presents a new solution to this problem. e hand shape feature is stable in a period of time, which has strong anticounterfeiting and anti-attack performance; the feature extraction algorithm is simple, and the recognition speed is high; and the finger knuckle print is rich in textures, just like fingerprints, which can remain stable for a long time and contain high individual differences. e fusion of the two features can achieve complementary advantages and improve the recognition accuracy, stability, and antiattack performance of the biometric identification system
Comparison of different methods. e proposed serial matching fusion method was compared with principal component analysis (PCA) global feature recognition, local binary pattern (LBP) operator, and traditional fusion based on fractional fusion. e recognition time and recognition rates are shown in Table 3. e proposed global + local serial fusion strategy achieved the highest recognition rate, and it was superior to local feature recognition and fractional weighted fusion in average recognition speed
Summary
Hand-based multibiometric recognition system occupies an important position in the field of biometric recognition. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. E hand shape feature is stable in a period of time, which has strong anticounterfeiting and anti-attack performance; the feature extraction algorithm is simple, and the recognition speed is high; and the finger knuckle print is rich in textures, just like fingerprints, which can remain stable for a long time and contain high individual differences. E fusion of the two features can achieve complementary advantages and improve the recognition accuracy, stability, and antiattack performance of the biometric identification system. Ere is no commercial biometric recognition system based on finger knuckle print, and the research results of finger knuckle print recognition are quite a few. Lin Zhang et al from Hong Kong Polytechnic University had designed the dorsal digital joint collection device, which
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