Static Face Recognition Using Hierarchical Model
Keywords:
Face recognition, Feature based methods, singular value decomposition Euclidean distance Original gray value matrixAbstract
Face is an important biometric feature for personal identification. Human beings easily detect and identify faces in a scene but it is very challenging for an automated system to achieve such objectives. Hence there is need to have reliable identification method for user interactions. A computer application which automatically identifies or verifies a person from a digital image or a video frame from a video source, is presented and it is done by comparing selected facial features from the image and a facial database. One of the retrieving method is Content based image retrieval (CBIR), which retrieves images on the basis of automatically derived features. This paper draws points from it but, focuses on a low-dimensional feature based indexing technique for achieving efficient and effective retrieval performance. A static appearance based retrieving system for face recognition referred to as hierarchical model is presented based on 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 and leads 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. It is a known 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 further it is pointed out that base images are sensitive to the composition of the face image. Finally the 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, a controlling parameter ‘α ’, which ranges from 0 to 1 is introduced a better result is achieved for α=0.4 when compared to the other value of ‘α” and it is also seen that it reduces classification redundancy.
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