Man in the Middle of Face Recognition System: using Skin Color and Template
T. Archana1, T. Venugopal2
1T. Archana, Assistant Professor, Department of CSE, University College of Engineering KU, Kothagudem (Telangana) India.
2Dr. T. Venugopal, Professor, Department of CSE, JNTUH College of Engineering Jagityal Nachupally, Karimnagar (Telangana) India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2601-2610 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4028129219/2019©BEIESP | DOI: 10.35940/ijeat.B4028.129219
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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: During last 10 years people are very much attracted to face recognition systems and they are very much eager to solve the issues related to face recognition system. It helped them very much in the field of electronics and uses over pattern unlocking and password entering system. There are numerous applications as for security, affectability and mystery. Detection of a face is the most significant and initial step of recognition framework. This article demonstrates a new method to face recognition system using color and template of an image. Whatever the background it may go to be, our system will detect the face, which is an important stage for face detection. The pictures utilized in this framework for Face detection are the color images, while the images used for the Face Recognition are the Gray images which are converted from color pictures. The illumination compensation technique is applied on all the images for removing the effect of light. The Red, Green, and Blue values of each pixel will be converted to YCbCr space. Based on the probability of each pixel in terms of Cb, Cr values, we extract the skin pixels from the query image,. The positive probability shows a “skin pixel”, while the negative probability shows “not a skin pixel”. Finally the face is projected. In face recognition, we used 4 templates of different sizes for Gabor image content extraction. Finally we employed the relevance feedback mechanism to retrieve the most similar images. If the user did not satisfy with the given results he can give the correct images to the system from the displayed images. Exploratory outcomes demonstrate that the demonstrated system is adequate to recognize face of a human face in a picture with an exactness of 94%.
Keywords: Probability function, Face detection, Gabor based templates, templates extraction, Face Recognition