Deep neural networks based wrist print region segmentation and classification

In recent years, biometric recognition based systems have become widespread. One of these is wrist-based recognition systems. In this study, wrist print based recognition system was developed by using near infrared (NIR) camera. Totally 220 NIR camera images taken from 10 for each both hands of 11 people. The obtained data set is allocated 70% (154 images) for training and 30% (66 images) for testing. The wrist regions are labeled on the training set images. Data sets were created with two different labeling methods. In the first data set, only the wrist regions were labeled and it was aimed to segment the wrist region from the image. In the second data set, the wrist images were labeled according to 22 classes and these classes were tried to be predicted. The labeled data was trained with YOLOV2 architecture supported by ResNet50 one of the deep neural network models. The trained model was tested with the remaining 30% of the data set. In the test process, the wrist region was determined in the NIR images with the trained model. As a results of the study, it was seen that the wrist regions were correctly detected in all first data set test images and the mean value of obtained similarity rates was 95.26%. In the test results of the second dataset, 92.43% classification success was obtained. Therefore, it can be said that the deep learning architectures ResNet and YOLO are effective in the segmentation of the wrist region.