English
资源说明：As a novel biometric, Finger-Knuckle-Print (FKP) has
received great interest in recent years, and has become a hot
research spot of biometric recognition. Due to its characteristic of
uniqueness, easy accessibility, none abrasion and abundant
texture, it has been widely applied to personal identification. But
the spare representation based FKP method has not been
reported yet. In this paper, a smooth l0 norm spare
representation model based FKP algorithm is proposed. Firstly,
an over-complete dictionary is constructed using the training
samples, and then Local Binary Pattern (LBP) operator is used
for feature extraction and dimension reduction. Finally, smooth l0
norm is used to solve the model, accelerate the recognition
process, and improve its efficiency. Experimental results on FKP
Database established by The Hong Kong Polytechnic University
show that the proposed method has achieved competitive good
results with the state-of-the-arts and has great potential in
practical applications
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