Transition from Holistic to Deep learning Face Recognition Methods
R. S. Sabeenian1, J. Harirajkumar2, Lizzie D’ Cruz3
1Dr.R.S.Sabeenian, Professor and Head in ECE Department in Sona College of Technology, Salem, Tamil Nadu, India.
2J.Harirajkumar, Associate Professor, Department of ECE, Sona College of Technology, Salem
3Lizzie D’ Cruz , Lecturer (SG) in Dr. B. R. Ambedkar Institute of technology.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3111-3116 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7974118419/2019©BEIESP | DOI: 10.35940/ijrte.D7974.118419

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Abstract: Face recognition, the fastest growing biometric technology of computer vision, made a breakthrough in the field of security, healthcare, access control and marketing etc. This technology helps in automatically discern and identify the faces for authentication by comparing available digital image of faces. Various algorithms have been developed for enhancing the performance of face recognition system. The face authentication system entails three major steps, face detection, feature extraction and face recognition. This paper provides some of the major milestones of face representation for recognition like holistic learning approach, feature based approach, hybrid approach and deep learning approach. The various techniques under these categories are reviewed. Finally, implemented face recognition using convolution neural network (CNN). In this method, the image is captured through webcam for the dataset preparation. The detection is carried out by CNN cascade, followed by face landmark and face embedding by FaceNet CNN. Recognition of face is performed after training the network. Implemented faces recognition successfully and accurately for smaller dataset.
Keywords: Face Recognition, Holistic Learning, Feature Extraction, Deep Learning, Convolution Neural Network.
Scope of the Article: Deep Learning.