Video Face Detection using Bayesian Technique
Seshaiah Merikapudi1, Shrishail Math2
1Seshaiah Merikapudi, Research Scholar, Department of CSE, SJCIT, Chickballapur (Karnataka), India.
2Dr. Shrishail Math, Professor, Department of CSE, SKIT, Bangalore (Karnataka), India.
Manuscript received on 21 May 2019 | Revised Manuscript received on 11 June 2019 | Manuscript Published on 27 June 2019 | PP: 78-85 | Volume-8 Issue-1C May 2019 | Retrieval Number: A10160581C19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a days, security based applications are developed widely and these systems are adopted in various real-time applications. Visual surveillance is considered as a most promising technique where certain objects can be detected, tracked and recognized using computer vision based approaches. In this field, face detection and recognition is considered as the important part of surveillance system. Several approaches have been developed for face recognition but existing approaches are applied on the face data. Recently, video face detection techniques are also introduced which provides more information to improve the security system. In this work, we emphasize on the detection of face, along with tracking and recognition using computer vision approach. In order to achieve this objective, first of all we utilized face detection and tracking approach using Kalman filtering. After face detection, we extract the combined features of the input image and stored the trained data. The learning process is developed using Bayesian learning approach. The proposed approach is implemented on benchmark datasets such as IARPA Janus Benchmark A (IJB-A), the YouTube Face repository and the Celebrity-1000 repository. A comparative performance evaluation is carried out which shows the robust performance of proposed approach.
Keywords: Bayesian Learning, Computer Vision, Face Detection, Kalman Filtering, Visual Surveillance.
Scope of the Article: Computer Vision