CNN Integrated With HOG For Efficient Face Recognition
R.Angeline1, Kavithvajen.K2, Toshita Balaji3, Malavika Saji4, Sushmitha.S.R5
1R.Angeline, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Kavithvajen.K, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Toshita Balaji, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4Malavika Saji, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
5Sushmitha.S.R, Department of Computer Science, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1657-1661 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2868037619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Human faces in the video are subject to illumination variation, out-of-focus blur and pose variations during face recognition process in various applications. The proposed system aims to eradicate the problems mentioned above. This is done by utilizing Histogram of Oriented Gradients algorithm as a feature descriptor to detect faces. The training data is composed of still images and blurred images. For the system to learn pose variations, an additional dataset of artificially aligned images is fed by using Face landmark estimations algorithm. Convolutional Neural network is trained, and effective face recognition is obtained. Thus, can make surveillance applications work efficiently
Keywords: Convolutional neural network, Face recognition, Histogram of oriented gradients, Support Vector machine.
Scope of the Article: Pattern Recognition