(untitled) (International Journal of Engineering and Computer Science, 2017)
Images are an important form of data and are used in almost every application. Some applications cannot use images directly due to the large amount of memory space needed to store these images. One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient invariant and non-redundant facial information. Motion information is used to ﬁnd the moving regions and probable eye region blobs are extracted by thresholding the image. These blobs reduce the search space for face veriﬁcation which is done by template matching. Experimental results for face detection show good performance even across orientation and pose variation to a certain extent. The face recognition is carried out by cumulatively summing up the Euclidean distance between the test face images and the stored database, which shows good discrimination for true and false subjects. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a main issue. The goal of this paper is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy, better response rate as an initial step for video surveillance. Solution is proposed based on performed tests on various face rich databases in terms of subjects, pose, emotions, race and light.