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Palmprint Recognition Based on Image Segmentation of Region of Interest

Palmprint Recognition Based on Image Segmentation of Region of Interest

QingE Wu, Weidong Yang
Copyright: © 2017 |Pages: 18
ISBN13: 9781522518846|ISBN10: 1522518843|EISBN13: 9781522518853
DOI: 10.4018/978-1-5225-1884-6.ch004
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MLA

Wu, QingE, and Weidong Yang. "Palmprint Recognition Based on Image Segmentation of Region of Interest." Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts, edited by Joan Lu and Qiang Xu, IGI Global, 2017, pp. 73-90. https://doi.org/10.4018/978-1-5225-1884-6.ch004

APA

Wu, Q. & Yang, W. (2017). Palmprint Recognition Based on Image Segmentation of Region of Interest. In J. Lu & Q. Xu (Eds.), Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts (pp. 73-90). IGI Global. https://doi.org/10.4018/978-1-5225-1884-6.ch004

Chicago

Wu, QingE, and Weidong Yang. "Palmprint Recognition Based on Image Segmentation of Region of Interest." In Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts, edited by Joan Lu and Qiang Xu, 73-90. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1884-6.ch004

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Abstract

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs coordinates by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction, and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

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