Age-dependent palm print recognition using convolutional neural network

Abstract

Biometric engineering is one of the most important and modern fields that affect human life directly. It can be considered as a new technology relatively, that is used for identity verification and/or the identification of persons depending on their physiological features, which include the morphological, biological, and characteristics of their behaviors. Many types of biometric recognitions are used depending on features of eyes, faces, hands (palm and/or fingerprints), voice, and many others. All the works before were focused on persons detection only but nor on their ages. This feature (age) considered as one of the not solved problems in the field of detection. In this paper, the palm recognition model consisted of many steps. The first step related to palm detection. Other techniques used to remove noisy portion from extracted image. After preparing images for training, a deep neural network represented by convolutional neural network is selected. A new idea and method (mechanism) is used. Palm print features' recognition algorithm depending on Convolutional Neural Network (CNN) is presented for recognizing individuals (persons recognition in different ages classes). Palm print technique is depended for different ages classes. The dataset is selected firstly for many known persons with different ages, for each person many palm image items are trained and tested using deep learning techniques. As mentioned, the CNN method is used for the training purpose, which means the recognition must be done depending on the CNN deep learning algorithm. The FAR and GAR factors are used to measure the performances of the recognition. The given results shown that the selection of the palm instead of other features types makes the recognition easier. More than 96% of the results were accurate. Also, the used algorithm which included the CNN had competitive performance, the algorithm succeeded to separate between the features according to the persons ages. The overall process is completed within 0.01×10-6second, which can be considered fast and suggested to be used in real time.