Verification of Biometric Traits using Deep Learning

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems including non-universality, noise, population coverage, vulnerability and intra-class variability for verification, authentication and identification of an individual. In this paper, the impact of deep learning in the field of biometrics is investigated where supervised learning is primarily involved in identifying biometric traits using Graphical User Interface. The trained deep learning system proposed is called MultiTraitConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without prior domain knowledge and classify the class of the biometric trait. A discriminative CNN training scheme is based on a combination of back-propagation algorithm and mini-batch Adam optimization method that is used for weights updating and learning rate adaption, respectively. To evaluate various CNN architecture data augmentation and dropout method training techniques have been used. A centralized database of different biometric trait images has been created using data sets of CASIA1 for iris trait, google 11K hand for palmprint trait, for face UTKFace is used. The accuracy of the proposed system is found to be 100%. Python library Keras is used to develop CNN model and TKinter is used to create GUI of the proposed system.