Identifying faces in images is easier but face identification in videos is more difficult than that in images because of low resolution, occlusion, non-rigid deformations, large motion, complex background and other uncontrolled conditions make the results of face detection and recognition unreliable. It is a challenging problem due to the huge variation in the appearance of faces in video to achieve accuracy. The main objective of proposed system is to efficiently identify faces even in case of occlusion like glasses, etc. which results in accuracy of system. Facial occlusions, due for example to sunglasses, hats, scarf, beards etc., can significantly affect the performance of any face recognition system. Unfortunately, the presence of facial occlusions is quite common in real-world applications especially when the individuals are not cooperative with the system such as in video surveillance scenarios. While there has been an enormous amount of research on face recognition under pose/illumination changes and image degradations, problems caused by occlusions are mostly overlooked. The focus of this paper is thus on facial occlusions, and particularly on how to improve the recognition of faces occluded by sunglasses and scarf. We propose an efficient approach which demonstrates state-of-the-art performance on streaming video face recognizing in various genres of videos and label them with the corresponding relevant names.

Face Detection, Face Recognition, Facial Occlusion, Streaming Video

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