In this paper, we propose a framework for low cost secure electronic voting system based on face recognition. Essentially Local Binary Pattern (LBP) is used for face feature characterization in texture format followed by chi-square distribution is used for image classification. Two parallel systems are developed based on smart phone and web applications for face learning and verification modules. The proposed system has two tire security levels by using person ID followed by face verification. Essentially class specific threshold is associated for controlling the security level of face verification. Our system is evaluated three standard databases and one real home based database and achieve the satisfactory recognition accuracies. Consequently our propose system provides secure, hassle free voting system and less intrusive compare with other biometrics.
This paper proposes an efficient and robust technique for face recognition. The proposed technique includes the
Daubechie's wavelet transform D10, Principal Component Analysis (PCA) and Multiscale fusion for face recognition.
Features are extracted using the PCA on original and multiscale images. The multiscale fusion is used to combine the
results of PCA and wavelet transformed PCA to achieve better performance. The main idea is to utilize the discriminant
information of various subbands rather than relying on a single scale. Multiscale experts are finally fused using the sum
rule. Extensive experimental results on the AT&T database show that recognition performance is improved by the