Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing
S.Kanaga Suba Raja1, M.Vivekanandan2, S.Usha Kiruthika3, S.Akila R .Janani Ayshwarya4
1Dr. S.Kanaga Suba Raja, Easwari Engineering College, Chennai, India.
2Mr.M. Vivekanandan*, Department of information Technology, Easwari Engineering College, Chennai, India.
3Dr.S. Usha Kiruthika is currently working as an Assistant Professor in the Department of Computer Science and Engineering in SRM Institute of Science and Technology, Kattankulathur Campus.
4S. Akila R.Janani Ayshwarya, Department of information Technology, Easwari Engineering College, Chennai, India.

Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4803-4807 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7676118419/2019©BEIESP | DOI: 10.35940/ijrte.D7676.118419

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 (

Abstract: One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are pre-processed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.
Keywords: Convolutional Neural Networks (CNN), K- Nearest Neighbor (KNN).
Scope of the Article: Signal and Image Processing.