DocumentCode :
3426820
Title :
Palmprint identification using Hausdorff distance
Author :
Li, Fang ; Leung, Maylor K H ; Yu, Xaozhou
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore
fYear :
2004
fDate :
1-3 Dec. 2004
Abstract :
Palmprint-based personal identification is regarded as an effective method for automatically recognizing a person´s identity. In addition, it requires no special hardware except a normal digital camera. This paper presents a new approach to identify palmprint using Hausdorff distance. Line edge map (LEM) of palmprint is extracted as the feature used for identifying. This system employs low-resolution palmprint images to achieve effective personal identification. In contrast to the existing methods, our approach is robust to noise, occlusion, and skewing.
Keywords :
biometrics (access control); edge detection; feature extraction; medical image processing; Hausdorff distance; line edge map; noise; occlusion; palmprint-based personal identification; skewing; Data mining; Digital cameras; Feature extraction; Fingerprint recognition; Fingers; Hardware; Image resolution; Image segmentation; Iris; Noise robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Circuits and Systems, 2004 IEEE International Workshop on
Print_ISBN :
0-7803-8665-5
Type :
conf
DOI :
10.1109/BIOCAS.2004.1454123
Filename :
1454123
Link To Document :
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