Efficient face detection and recognition using block independent component analysis and clustering

This paper presents an efficient image retrieval system called BICA-C for face recognition. This system takes an image as an input query and retrieves images based on its image content. Content Based Image Retrieval (CBIR) is an approach for retrieving semantically-relevant images from an image database based on extracted image features. Block Independent Component Analysis (B-ICA) is a popular feature extraction method. This paper proposes a scheme to enhance the performance of B-ICA based feature extraction by the utilization of k-means clustering technique. The proposed scheme consists of two stages. First stage is the offline training process where the extracted features in the database is clustered using k-means clustering and then the second stage is the online testing where the obtained query image is compared with the clustered images and is classified as intruder or authenticated person based on the similarity measure.