Dong En-Zeng( ) a)b) † , Chen Zeng-Qiang( ) a) , Chen Zai-Ping( ) b) , and Ni Jian-Yun( ) b)

No abstract

Fingerprint feature detection is one of the key techniques of automatic fingerprint identification. There are some problems of rotation and shift in the present fingerprint feature detection. This paper has given an algorithm of bifurcation detection based on neural network template matching. Correlative matching is to calculate the correlative value between template and target image according to a certain criteria when the template moves on the target image. The center of best matching is considered as fingerprint feature. The criteria of scaling the matching value is regarded as optimization function. Thus, matching recognition is converted into function optimization. The designed matcher based on neural network template can keep rotation invariant when the target image has the rotation of a certain angle. Finally, bifurcation can be extracted by multi-input and singleoutput there-layer feed forward neural network. The experimental results based on FVCX, have shown that the algorithm has rotation and shift invariant.

This paper proposed a novel 3D palmprint recognition algorithm by combining 3D palmprint features using D-S fusion theory. Firstly, the structured light imaging is used to acquire the 3D palmprint data. Secondly, two types of unique features, including mean curvature feature and Gaussian curvature feature, are extracted. Thirdly, the belief function of the mean curvature recognition and the Gaussian curvature recognition was assigned, respectively. Fourthly, the fusion belief function from the proposed method was determined by the Dempster-shafer (D-S) fusion theory. Finally, palmprint recognition was accomplished according to the classification criteria. A 3D palmprint database with 1000 range images from 100 individuals was established, on which extensive experiments were performed. The results show that the proposed method 3D palmprint recognition is much more robust to illumination variations and condition changes of palmprint than MCR and GCR. Meanwhile, by fusing mean curvature and Gaussian curvature feature, the experimental results are promising (the average equal error rate of 0.404%). In the future, imaging technique needs further improvement for a better recognition performance.

No abstract