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Exact segmentation of fingerprint image is very important for fingerprint singular points and minutiae features extraction. In this paper, a method for fingerprint image segmentation is proposed based on Support Vector Machine (SVM). The fingerprint image is broken into 16*16 prospects blocks and background blocks. The block average gray, block gray variance, block contrast and the largest peak of...
The use of efficient classification methods is necessary for automatic fingerprint recognition systems. This paper introduces an approach to fingerprint classification by using Self-Organizing Maps (SOM). In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. The concept of `certainty' is introduced and used in the...
Core point detection is very important in fingerprint classification and matching process. Usually fingerprint images have noisy background and the local orientation field also changes very rapidly in the singular point area. It is difficult to locate the singular point precisely. In this paper, we present a new algorithm for optimal core point detection using improved segmentation and orientation...
This paper proposes a robust feature level based fusion classifier for face and fingerprint biometrics. The proposed system fuses the two traits at feature extraction level by first making the feature sets compatible for concatenation and then reducing the feature sets to handle the 'problem of curse of dimensionality'; finally the concatenated feature vectors are matched. The system is tested on...
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