IoT Based Smart Glasses with Facial Recognition for People with Visual Impairments
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 9 |
| Year of Publication : 2023 |
| Authors : Swapna Choudhary, Nitin Dhote, Ashwini A. Deshpande, Ansh Sambhariya, Poorvi K. Joshi |
10.14445/23488379/IJEEE-V10I9P114
How to Cite?
Swapna Choudhary, Nitin Dhote, Ashwini A. Deshpande, Ansh Sambhariya, Poorvi K. Joshi, "IoT Based Smart Glasses with Facial Recognition for People with Visual Impairments," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 9, pp. 154-159, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I9P114
Abstract:
The paper presents a low-cost intelligent smart glasses system that uses a Raspberry Pi 4 board, a camera module, and an ultrasonic sensor to capture images and audio from the environment and process them using computer vision and natural language processing techniques. A working prototype of intelligent eyewear will enable people with vision impairments to recognize others in their sight and learn about potential dangers. This will be made possible by face recognition technology and distance detection capabilities. A built-in sensor that emits ultrasonic waves in the direction the user is moving while scanning a maximum of 5 to 6 meters away at an angle of 30 degrees. This new system could resolve some of the most significant issues that blind people still face. Finally, a message is sent to blind people, informing them of the person in front of them using the sounds associated with each individual in the database. The proposed system consists of a depth camera to gather depth data from the environment, an ultrasonic rangefinder made up of an ultrasonic sensor, and an embedded CPU board serving as the central processing module. The embedded CPU handles tasks like depth image processing, data fusion, AR rendering, and guiding sound synthesis. AR glasses are used to show the visual enhancement information, and an earphone is used to play the guiding sound. This proposed system is designed to assist people with visual impairments by enabling them to recognize people in front of them and detect obstacles. The technology utilized in this system includes facial recognition and distance detection capabilities, which are made possible by using Raspberry Pi and Pi cameras. The system also incorporates an ultrasonic sensor and a Raspberry Pi powered by a 5V power supply to detect and avoid obstacles.
Keywords:
Smart glasses, Raspberry Pi 4, Facial recognition, Ultrasonic sensor, Vision impairments, Computer vision, Natural language processing.
References:
[1] V. Bindhu et al., “International Conference on Communication, Computing and Electronics Systems,” Proceedings of ICCCES 2020, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Tsung-Yi-Lin et al., “Microsoft COCO Common Objects in Context,” European Conference on Computer Vision, pp. 740-755, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shrilekha Bangar, Preetam Narkhede, and Rajashree Paranjape, “Vocal Vision for Visually Impaired,” The International Journal of Engineering and Science, vol. 2, no. 3, pp. 1-7, 2013.
[Google Scholar] [Publisher Link]
[4] Alessio Carullo, and Marco Parvis, “An Ultrasonic Sensor for Distance Measurement in Automotive Applications,” IEEE Sensors Journal, vol. 1, no. 2, pp.143-147, 2001.
[Google Scholar] [Publisher Link]
[5] Akshansh Sharma et al., “Python: The Programming Language of Future,” International Journal of Innovative Research in Technology, vol. 6, no. 12, pp. 115-118, 2020.
[Google Scholar] [Publisher Link]
[6] Eben Upton, and Gareth Halfacree, The Raspberry Pi Camera Module, Raspberry Pi® User Guide, 4, Wiley Online Library, 2016.
[CrossRef] [Publisher Link]
[7] N.A. Azis et al., “Evaluation of Text-to-Speech Synthesizer for Indonesian Language Using Semantically Unpredictable Sentences Test: IndoTTS, eSpeak, and Google Translate TTS,” 2011 International Conference on Advanced Computer Science and Information Systems, Jakarta, Indonesia, pp. 237-242, 2011.
[Google Scholar] [Publisher Link]
[8] N. Ifeanyi, O. Ikenna, and O. Izunna, “Text-To-Speech Synthesis (TTS), International Journal of Research in Information Technology, vol. 2, no. 5, pp. 154-163, 2014.
[Google Scholar]
[9] Joyassree Sen et al., “Face Recognition Using Deep Convolutional Network and One-Shot Learning,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 4, pp. 23-29, 2020.
[CrossRef] [Publisher Link]
[10] I. Indriyani et al., “Using Convolutional Neural Networks to Detect Races in Human Faces Using MATLAB Programming,” International Journal of Engineering Trends and Technology, vol. 71, no. 6, pp. 66-74, 2023.
[CrossRef] [Publisher Link]
[11] Reinhard Klette, Concise Computer Vision, An Introduction into Theory and Algorithms, Springer, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Bernd Jähne, and Horst Haußecker, Computer Vision and Applications: A Guide for Students and Practitioners, Academic Press, 2000.
[Google Scholar] [Publisher Link]
[13] M. Naveenkumar et al., “OpenCV for Computer Vision Applications,” Proceedings of National Conference on Big Data and Cloud Computing (NCBDC’15), pp. 52-56, 2015.
[Google Scholar] [Publisher Link]
[14] Earle Kit M. Sanchez et al., “Three Triangle Method for Face Recognition Using Dlib and OpenCV,” 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] G. Madhan Kumar et al., “Facial Recognition Smart Glasses for Visually Challenged Persons,” International Journal of Engineering Science and Computing, 2020.
[Google Scholar]
[16] Naveen Tiwari et al., “Face Recognition Smart Glasses for Visually Challenged Persons,” International Journal of Research and Development in Applied Science and Engineering (IJRDASE), vol. 21, no. 1, 2021.
[Google Scholar] [Publisher Link]
[17] Narayan T. Deshpande, and S. Ravishankar, “Face Detection and Recognition Using Viola-Jones Algorithm and Fusion of PCA and ANN,” Advances in Computational Sciences and Technology, vol. 10, no. 5, pp. 1173- 1189, 2017.
[Publisher Link]
[18] Kavia, and Manjeet Kaur, “A Survey Paper for Face Recognition Technologies,” International Journal of Scientific and Research Publications, vol. 6, no. 7, pp. 441-445, 2016.
[Publisher Link]
[19] A.D. Chandan et al., “Survey Paper on Vehicle Security Using Facial Recognition & Password,” SSRG International Journal of Electronics and Communication Engineering, vol. 9, no. 6, pp. 5-9, 2022.
[CrossRef] [Publisher Link]
[20] Hirak Dipak Ghael, L. Solanki, and Gaurav Sahu, “A Review Paper on Raspberry Pi and its Applications,” International Journal of Advances in Engineering and Management (IJAEM), vol. 2, no. 12, pp. 225-227, 2020.
[Publisher Link]
[21] Ankita Bhuniya et al., “Smart Glass for Blind People,” AMSE Journals, vol. 38, no. 1, pp. 102-110, 2017.
[Google Scholar] [Publisher Link]
[22] Sanjay Rajaraman, and Ps Shivapriya, “A-Eye: Computer Vision and Deep Learning Based Smart-Glasses with Edge Computing for Visually Impaired,” 2023 3rd International Conference on Artificial Intelligence and Signal Processing (AISP), pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Peer Azmat Shah, H. Ali Rashed Ali Alhefeiti, and R. Hussain Mohamed Ali Alrayssi, “Machine Learning-Based Smart Assistance System for the Visually Impaired,” 2023 9th International Conference on Information Technology Trends (ITT), pp. 139-144, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mukhriddin Mukhiddinov, and Jinsoo Cho, “Smart Glass System Using Deep Learning for the Blind and Visually Impaired,” Electronics 2021, vol. 10, no. 22, pp. 1-30, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] K. Shruthi et al., “A Study on Face, Expression, Object and Character Recognition to Implement An Aid for the Visually Impaired,” Proceedings of the International Conference on IoT Based Control Networks & Intelligent Systems (ICICNIS), pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] A. Pentland, and T. Choudhury, “Face Recognition for Smart Environments,” Computer, vol. 33, no. 2, pp. 50-55, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[27] A. Hafeez Ali et al., “A Google Glass Based Real-Time Scene Analysis for the Visually Impaired,” IEEE Access, vol. 9, pp. 166351- 166369, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Stefan van der Walt, S. Chris Colbert, and Gael Varoquaux, “The NumPy Array: A Structure for Efficient Numerical Computation,” Computing in Science & Engineering, vol. 13, no. 2, pp. 22-30, 2011.
[CrossRef] [Google Scholar] [Publisher Link]