Multimodal biometrics has engrossed a lot of interest in recent years as it offers a more dependable scheme for verification and authentication. Multimodal biometrics includes a combination of information from different biometric modalities. This research work simulates a novel method with a view of attendance management using ear and tongue images. Principal Component Analysis was employed for feature extraction of the biometric modalities and Self Organizing Feature Map was used for training and testing of the system. The method was evaluated using six thousand ear images and four thousand tongue images. The acquired images were preprocessed and features extracted were fused at this stage. The fusion results of ear and tongue images demonstrated better performance for attendance management.

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%.