Human identification using palm print images

Palm recognition is one of the research areas which is considered, in recent years. In this thesis, we introduce a new method for human palmprint identification with local binary pattern and Co-occurrence matrix. First, the palm images are preprocessed with morphological technics. Then feature extraction is applied for images. We used local binary pattern (LBP) and gray level Co-occurance matrix (GLCM) for desired features. This approach is tested for 20 people and there are 1, 2, 3 and 4 images from each people. Our method is compared with PCA method. The result shows that proposed method have high accuracy and good performance (%92) for palmprint recognition.