Detection of Human Faces in Video Sequences using NGLBP Feature
A. Jainul Fathima1, P. Ithaya Rani2, T. Hari Prasath3
1A. Jainul Fathima, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2P. Ithaya Rani, Department of Computer Science and Engineering, Sethu Institute of Technology, Kariapatti (Tamil Nadu), India.
3T. Hari Prasath, Department of Electronics and Communication, Kamaraj College of Engineering and Technology, Virdhunagar (Tamil Nadu), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 1031-1036 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11071284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1107.1284S219
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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: Machine analysis of face detection is an interesting topic for study in Human-Computer Interaction. The existing studies show that discovering the position and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. This paper suggests a method to detect the location of face area using recently developed YouTube Video face database. In this work, each frame is formulated by normalization technique and separated into overlapping blocks. The Gabor filter is tuned to extract the Gabor features from the individual blocks. The averaged Gabor features are manipulated and local binary pattern histogram features are extracted. The extracted patterns are passed to the classifier with training images for face region identification. Our experimental results on YouTube video face database exhibits promising results and demonstrate a significant performance improvement when compared to the existing techniques. Furthermore, our proposed work is uncaring to head poses and sturdy to variations in illumination, appearance and noisy images.
Keywords: Ensemble Classifier, Gabor Wavelet, Human Computer Interaction, Local Binary Pattern, Normalization.
Scope of the Article: Human Computer Interactions