Abstract
Most of these methods succeeded to achieve the invariance against object translation, rotation and scaling, but that could not neutralize the bright background effect and non-uniform light on the quality of the generated features. To eliminate the limit that the recent subspace learning methods for facial feature extraction are sensitive to the variations of orientation, position and illumination in capturing palmprint images, a novel palmprint feature extraction approach is proposed. Palmprint images are decomposed into a sequence of binary images using a Modified Pulse-Coupled Neural Network (M-PCNN), and then the information entropy of each binary image are calculated and regarded as features. A classifier based Support Vector Machine (SVM) is employed to implement recognition and classification. Simultaneously, it overcomes the disadvantage of standard PCNN model with high number of parameters. Theoretical and experimental results show that the proposed approach is robust to the variations of orientation, position and illumination conditions in comparison with other methods based subspace.
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Huai, WJ., Shang, L. (2013). A Novel Method for Palmprint Feature Extraction Based on Modified Pulse-Coupled Neural Network. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_34
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DOI: https://doi.org/10.1007/978-3-642-39482-9_34
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