Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

In this thesis, we used palm print images for human identification. This thesis contains four steps. First, the features of the palm print images are extracted by pretrained network that uses the deep learned network. Thre pretrained networks such as Googlenet squesse net and Alexnet are used to extract the features from the images. Then the best features were selected by using the Gray-Wolf optimization method. In the third step, these features are used in the nearest neighborhood method for recognition of the dataset to be used in the test data. Finally, we evaluated the results with recognition rate that calculated the percentage of the recognitions of the mistake index and correct index from the dataset. The aim of this thesis is to use the Gray-Wolf Optimization method and deep learning to get high recognition rate. In this study we used two famous dataset such as Polytechnic Hong Kong university dataset and Tongji Contactless datasets, the recognition rate for the different methods is evaliated and tested. We are showing that the proposed method has a very high performance in the recognition rate than other different methods such as principle compomnent analysis, Local binary pattern and laplacian of gaussian gabor transform. The recognition rate from the proposed method we obtain 96.72.