Open AccessAN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
Author(s) -
LAWRENCE O. OMOTOSHO,
Ibrahim Kazeem Ogundoyin,
OLAJIDE ADEBAYO,
JOSHUA O. OYENIYI
Publication year - 2021
Publication title -
journal of engineering studies and research
Language(s) - English
Resource type - Journals
eISSN - 2344-4932
pISSN - 2068-7559
DOI - 10.29081/jesr.v27i2.276
Subject(s) - computer science , biometrics , artificial intelligence , convolutional neural network , iris recognition , word error rate , pattern recognition (psychology) , normalization (sociology) , feature extraction , facial recognition system , sociology , anthropology
Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.