Wavelet Based Modern Finger Knuckle Authentication

Biometrics is a prominent technology for accurate and safe detection of claim identity. This paper explores new biometrics trait named finger knuckle (FK) for authentication. Finger Knuckle has unique bending and makes a distinctive biometric identifier. We use middle part of back surface of finger for recognition. The system consists of proposed prototype finger knuckle capturing device, own FK images acquired from this device and Kekre Wavelet Transform based feature for matching. Finger knuckle image capturing device is modified structurally using SolidWorks13. Feature extraction is based on Kekre Wavelet Transform which gives energy coefficient as unique features for matching. Experimental tests are performed on right index and middle finger knuckle of 50 users of own FK image data base acquired from proposed device and standard Hong Kong Polytechnic University (Poly U) database. Proposed Kekre Wavelet Transform (KWT) algorithm is tested on both FK database shows improved recognition accuracy of 90% as compared to conventional wavelet based method. The experimental results shows that wavelet based fusion of local and global features shows improved recognition accuracy of 92.5%, which is more than integration of local features only. The local features namely local orientation, local phase, and phase congruency are integrated with one global feature of Fourier transform coefficient