Evaluation of relevance vector machine classifier for a real-time face recognition system

Face recognition has found a variety of applications in consumer electronics, such as laptops, smart phones, home security systems, home automation systems and many more. Machine learning is one of the important concepts, required for designing any pattern recognition system, including the proposed real-time face recognition system. Relevance vector machine is considered as one of the most recent machine learning algorithms reported in literature. In this paper, design and evaluation of Relevance Vector Machine classifier architectures for a real-time face recognition system using Histogram of Oriented Gradient features is proposed. In order to assess the performance of system designed, AT&T database of faces are initially considered, followed by the performance evaluation using real-time face inputs from the system camera. 81.25–97.00% recognition accuracy is obtained on using the proposed system and the proposed work can be easily extended for various other pattern recognition systems too.