Empirical study of light source selection for palmprint recognition
Pattern Recognition Letters
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Lines provide important information and line detection is crucial in many applications. However, most of the existing algorithms focus only on the extraction of line positions, ignoring line thickness. In this thesis, we aim to address this issue. We first propose a novel wide line detector to extract the line entirely. In contrast with the traditional directional-derivative-based edge and line extraction method, our wide line detector is based on isotropic nonlinear filtering and consequently is more robust to noise. We develop an approach for dynamic selection of parameters of the wide line detector. A sequence of tests is conducted on a variety of image samples. The experimental results demonstrate that the proposed wide line detector works very well for a range of images, especially for those where the width of lines varies greatly and where the lines run close together or cross each other. We then address the first application of our wide line detector: palm-line based palmprint recognition. We present a novel palm-line feature extraction method for personal identification. Compared to previous work, the proposed method extracts not only structure features but also strength features of palm lines. We also develop an experimental scheme to optimize the parameter combination for the proposed method. An extensive test is conducted on a public palmprint database. Experimental results show that the performance of the proposed method is comparable with the state-of-the-art algorithms of palmprint identification and thereby palm-line features can be used to recognize palmprints. Finally, we for the first time attempt to extract tongue cracks, one of pathological features in tongue diagnosis. We propose a framework for automatic tongue crack extraction. We derive a tongue crack detection scheme based on the wide line detector. Due to the large range of widths of tongue cracks, the maximum widths of cracks vary greatly with different tongue images and consequently the size of the detector used should be very different. To implement the proposed scheme totally automatically, we design an adaptive algorithm of line width estimation. The proposed scheme has been tested on a set of typical cracked tongue samples and our experimental results show the promising performance of our automatic tongue crack extraction scheme.