A multimodal biometric system using partition based DWT and rank level fusion

Multimodal biometrics is often used as it solves the issues of unimodal biometrics like non-universality, noisy data and security. Face and palmprint of a person do not change easily with time. To extract both local and global features of an image, we propose a partition based DWT (Discrete Wavelet Transform) and 2DPCA (2Dimensional Principal Component Analysis) for feature extraction. The fusion of face and palm print is done at rank level using Highest rank, Borda count and Logistic Regression methods. The proposed method is better than 2DPCA (upto 1% higher rate) and DWT (upto 3% higher rate) in terms of average recognition rate for Unimodal systems. For Multimodal approach, the proposed method gives an equal error rate of 0.08 when compared to Unimodal approach of 0.24 for palm and 0.16 for face.