Human identification with palmprint based on local binary pattern

Palmprint recognition is one of the research areas is considered in recent years. In this thesis, we introduce a new method for human Palmprint identification system with local binary pattern. First, the palm images are preprocessing with morphological technics. Then the feature extraction for images is applied. We used local binary pattern and gray level Co-occurance matrix for desired features. This approach is tested for 500 people and there are 1, 2, 3... 7 image from each people. Our method is compared with PCA method and Wavelet continuous transformation. The result shows that proposed method is efficiency and good performance for Palmprint recognition.