Robust Point-Based Feature Fingerprint Segmentation Algorithm

Abstract

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 that of Harris point in background area. The underlying mechanism for this segmentation method is that boundary ridge endings are inherently stronger Harris corner points. Some Harris points in noisy blobs might have higher strength, but it can be filtered as outliers using corresponding Gabor response. The experimental results proved the efficiency and accuracy of new method are markedly higher than those of previously described methods.

Direct Pore Matching for Fingerprint Recognition

“…Most existing AFRS utilize the minutiae (the endings and bifurcations of ridges, belonging to level-2 features) to recognize fingerprints and to identify persons [11][12][13][14]. However, as people's desire for higher security levels keeps increasing, it is highly necessary to base the recognition of fingerprints on more features, but not merely minutiae.…”

Region-Based Watermarking of Biometric Images: Case Study in Fingerprint Images

“…This scheme is applied to fingerprint images that are used by one of the most employed and widely deployed biometric systems. To extract the ROI of such images, known as ridges area, we modified the segmentation technique proposed by Wu et al [14]. The proposed scheme is used with the classical optimum, multiplicative watermark detection.…”

“…In this work, Harris corner point features method [14] is adopted to extract the ridges area of fingerprint images. The Harris corner detector is based on the local autocorrelation function of a signal; where the local autocorrelation function measures the local changes of the signal with patches shifted by a small amount in different directions [15].…”

“…The Harris corner detector is based on the local autocorrelation function of a signal; where the local autocorrelation function measures the local changes of the signal with patches shifted by a small amount in different directions [15]. Wu et al found in [14] that the strength of the Harris point in the ridges area is much higher than that of the background area. However, the authors used different thresholds, which are determined experimentally for each image.…”

“…Note that fingerprint-based systems are regarded as the most powerful and widely deployed biometric systems. To extract the ROI of such images, referred here to as ridges area, the segmentation technique proposed by Wu et al [14] is modified in order to use adaptive thresholding. For sake of completeness, the watermark is embedded into the discrete wavelet transform (DWT) and discrete Fourier transform (DFT), where the DWT coefficients are statistically modeled by the generalized Gaussian distribution (GGD) and the DFT coefficients are modeled by the Weibull distribution.…”