A Spectral Domain Dominant Feature Extraction Algorithm for Palm-print Recognition

International Journal of Image Processing

Shaikh Anowarul Fattah¹  Hafiz Imtiaz¹ 

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关键词: Spectral Feature Extraction;    Principal Component analysis (PCA);    Two-Dimensional Fourier Transform;    Classification;    Palm-print Recognition;    Entropy;   

DOI  :  

来源: Computer Science Journals

【 摘 要 】

In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two dimensional Fourier transform within those spatial modules. A dominant spectral feature selection algorithm is proposed, which offers an advantage of very low feature dimension and results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.

【 授权许可】

Unknown