Fast sparse representation for Finger-Knuckle-Print recognition based on smooth L0 norm

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 l
₀
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 l
₀
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.