Biometric-Based Unimodal and Multimodal Person Identification with CNN Using Optimal Filter Set

The convolution neural network (CNN) has brought about a drastic change in the field of image processing and pattern recognition. The filters of CNN model correspond to the activation maps that extract features from the input images. Thus, the number of filters and filter size are of significant importance to learning and recognition accuracy of CNN model-based systems such as the biometric-based person authentication system. The present paper proposes to analyze the impact of varying the number of filters of CNN models on the accuracy of the biometric-based single classifiers using human face, fingerprint and iris for person identification and also biometric-based super classification using both bagging and programming-based boosting methods. The present paper gives an insight into the optimal set of filters in CNN model that gives the maximum overall accuracy of the classifier system.