A computer application which automatically identifies or verifies a person from a digital image or a video frame from a video source, one of the ways to do this is by com-paring selected facial features from the image and a facial database. Content based image retrieval (CBIR), a technique for retrieving images on the basis of automatically derived features. This paper focuses on a low-dimensional feature based indexing technique for achieving efficient and effective retrieval performance. An appearance based face recognition method called singular value decomposition (SVD) is proposed in this paper and is different from principal component analysis (PCA), which effectively considers only Euclidean structure of face space for analysis which lead to poor classification performance in case of great facial variations such as expression, lighting, occlusion and so on, due to the fact the image gray value matrices on which they manipulate are very sensitive to these facial variations. We consider the fact that every image matrix can always have the well known singular value decomposition (SVD) and can be regarded as a composition of a set of base images generated by SVD and we further point out that base images are sensitive to the composition of face image. Finally our experimental results show that SVD has the advantage of providing a better representation and achieves lower error rates in face recognition but it has the disadvantage that it drags the performance evaluation. So, in order to overcome that, we conducted experiments by introducing a controlling parameter ‘α’, which ranges from 0 to 1, and we achieved better results for α=0.4 when compared with the other values of ‘α’. Key words: Singular value decomposition (SVD), Euclidean distance, original gray value matrix (OGVM).

Full text: IJEIR-294 Final.pdf