Robust Point-Based Feature Fingerprint Segmentation Algorithm

Abstract. A critical step in automatic fingerprint recognition is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract blockwise features. Supervised method usually first extracts point features like coherence, average gray level, variance and Gabor response, then a Fisher linear classifier is chosen for classification. This method provides accurate results, but its computational complexity is higher than most of unsupervised methods. This paper proposes using Harris corner point features to discriminate foreground and background. Shifting a window in any direction around the corner should give a large change in intensity. We observed that the strength of Harris point in the foreground area is much higher than tha...

• Type: Conference
• Year: 2007
• Where: ICB
• Authors: Chaohong Wu, Sergey Tulyakov, Venu Govindaraju
• Keywords: Biometrics, Gabor Response, Harris Corner Points, Harris Points, ICB 2007