A comparative study on palmprint recognition

Palmprint recognition uses the palm of a person as a biometric for identifying or verifying the human beings. The palmprint contains a number of distinctive features such as principal lines, wrinkles, ridges and minutiae. Therefore, it is appropriate to use feature extraction techniques in order to extract line, texture, statistics and multiple representations. This thesis presents a comparative study on palmprint recognition using different approaches to extract palmprint features. Appearance-based approaches such as Principal Component Analysis, statistical approaches such as Local Binary Patterns, transform-based approaches such as Discrete Cosine Transform and other approaches such as Log-Gabor filters have been investigated and evaluated on PolyU palmprint database. The experimental results on both right and left palmprint databases demonstrate that Local Binary Patterns approach is a good texture descriptor which achieves the best recognition accuracy compared to other methods. Keywords: Log-Gabor, Discrete Cosine Transform, Local Binary Patterns, Principal Component Analysis