Software Framework for Face Recognition System Using Amazon Rekognition
Abstract
This study presents the development and implementation of a software framework for a face recognition system using Amazon Rekognition, a cloud-based image and video analysis service provided by Amazon Web Services (AWS). The face recognition system is designed to offer a scalable, accurate, and efficient solution for identity verification and authentication, primarily targeting applications in security, access control, and user authentication systems. The framework integrates various AWS services, including Amazon S3 for image storage, Amazon Lambda for serverless computation, Amazon DynamoDB for database management, and API Gateway for interaction between the front-end application and backend services. During the implementation, Pycharm Integrated Development Environment (IDE) was used to develop user interface. The methodology employed in this study includes the collection of a dataset comprising 500 images for training and 200 images for testing. Images were gathered from primary and secondary sources, and preprocessing techniques such as face detection, normalization, and augmentation were applied to ensure the quality and consistency of the dataset. During the training phase, Amazon Rekognition’s IndexFaces API was used to extract unique facial features and store them for subsequent comparisons. In the testing phase, the SearchFacesByImage API was used to evaluate the system’s performance in identifying and verifying faces. The proposed framework was evaluated for performance metrics such as scalability, processing time, and accuracy, and compared with other face recognition models to determine its competitive advantages. By leveraging cloud-based technologies and serverless architectures, the system exhibits high scalability, minimal latency, and cost- efficiency, making it ideal for real-time, large-scale deployments. Additionally, the framework is flexible and can be integrated into various application domains, such a
Keywords
Amazon Rekognition
API Gateway
AWS
DynamoDB
Face Rekognition
References
Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, present, and future of face recognition: A review. Electronics, 9(8), 1188. Ahmed, A., Guo, J., Ali, F., Deeba, F., & Ahmed, A. (2018, May). LBPH based improved face recognition at low resolution. In 2018 international conference on Artificial Intelligence and big data (ICAIBD) (pp. 144-147). IEEE. Alhanaee, K., Alhammadi, M., Almenhali, N., & Shatnawi, M. (2021). Face recognition smart attendance system using deep transfer learning. Procedia Computer Science, 192, 4093- Almishal, A., & Youssef, A. E. (2014). Cloud service providers: A comparative study. International journal of computer applications & information technology, 5(2), 46-52. Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: an overview. In Journal of physics: conference series (Vol. 1142, p. 012012). IOP Publishing. Bambharolia, P. (2017, May). Overview of Convolutional Neural Networks. In Proceedings of the International Conference on Academic Research in Engineering and Management, Monastir, Tunisia (pp. 8-10). Barnouti, N. H., Al-Dabbagh, S. S. M., & Matti, W. E. (2016). Face recognition: A literature review. International Journal of Applied Information Systems, 11(4), 21-31. Bezukladnikov, I., Kamenskih, A., Tur, A., Kokoulin, A., & Yuzhakov, A. (2021). Technology: Person Identification. In Handbook of smart cities (pp. 653-686). Cham: Springer International Publishing. Bhele, S. G., & Mankar, V. H. (2012). A review paper on face recognition techniques. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(8), 339-346. Boesch, G. (2021). Deep Neural Network: The 3 Popular Types (MLP, CNN, and RNN). viso. ai. Buciu, I., & Gacsadi, A. (2016). Biometrics systems and technologies: A survey. International Journal of Computers Communications & Control, 11(3), 315-330. Cao, K., Rong, Y., Li, C., Tang, X., & Loy, C. C. (2018). Pose-robust face recognition via deep residual equivariant mapping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5187-5196). Damale, R. C., & Pathak, B. V. (2018, June). Face recognition based attendance system using machine learning algorithms. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 414-419). IEEE. De Carrera, P. F., & Marques, I. (2010). Face recognition algorithms. Master's thesis in Computer Science, Universidad Euskal Herriko, 1. Dharrao, D. S., & Uke, N. J. (2019). Fractional Krill–Lion algorithm based actor critic neural network for face recognition in real time surveillance videos. International Journal of Computational Intelligence and Applications, 18(02), 1950011. Dospinescu, O., & Popa, I. (2016). Face detection and face recognition in android mobile applications. Informatica Economica, 20(1), 20. Dutta, P., & Dutta, P. (2019). Comparative study of cloud services offered by Amazon, Microsoft & Google. International Journal of Trend in Scientific Research and Development, 3(3), 981-985. Elrefaei, L. A., Alharthi, A., Alamoudi, H., Almutairi, S., & Al-rammah, F. (2017, March). Real-time face detection and tracking on mobile phones for criminal detection. In 2017 2nd International Conference on Anti-Cyber Crimes (ICACC) (pp. 75-80). IEEE. Faisal, F., & Hossain, S. A. (2019, August). Smart security system using face recognition on raspberry Pi. In 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 1-8). IEEE. Fulzele, V., Kirad, P., Dubey, C., Kulkarni, Y., & Thatte, S. (2021, May). Utilizing cloud capabilities for face detection and face recognition during COVID-19: Comparative Analysis. In Proceedings of the International Conference on Smart Data Intelligence (ICSMDI 2021). Galiano, A., Massaro, A., Barbuzzi, D., Legrottaglie, M., Vitti, V., Pellicani, L., & Birardi, V. (2016). Face recognition system on mobile device based on web service approach. Int. J. Comput. Sci. Inf. Technol.(IJCSIT), 7(4), 2130-2135. Ganakwar, D. G., & Kadam, V. K. (2019, March). Face detection using boosted cascade of simple feature. In 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) (pp. 1-5). IEEE. Gupta, Y., Prasad, A., Touti, S., Sachdev, K., Jaiswal, V., & Naranje, V. (2021, March). Real- time face recognition: A survey. In 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 430-434). IEEE. Hapani, S., Prabhu, N., Parakhiya, N., & Paghdal, M. (2018, August). Automated attendance system using image processing. In 2018 fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-5). IEEE. Honguntiker, K. P. Analysis of Facial Expressions with Amazon Rekognition. Available at SSRN 4597968. Horng, S. J., Supardi, J., Zhou, W., Lin, C. T., & Jiang, B. (2020). Recognizing very small face images using convolution neural networks. IEEE Transactions on Intelligent Transportation Systems, 23(3), 2103-2115. Indla, R. K. (2021). An overview on amazon rekognition technology. Islam, M. A., Ahmed, M. T., Hossain, M. I., Kabir, M. H., & Roy, S. (2023). Face recognition based physical layer security system for next-generation wireless communication. Islam, N., & Rehman, A. U. (2013, September). A comparative study of major service providers for cloud computing. In proceedings of 1st International Conference on Information and Communication Technology Trends, At Karachi, Pakistan. Jin, K., Xie, X., Wang, F., Han, X., & Shi, G. (2019, July). Human identification recognition in surveillance videos. In 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 162-167). IEEE. Junered, M. (2010). Face recognition in mobile devices. Karthick, S., Selvakumarasamy, S., Arun, C., & Agrawal, P. (2021). WITHDRAWN: automatic attendance monitoring system using facial recognition through feature-based methods (PCA, LDA). Khan, I., Dewangan, B., Meena, A., & Birthare, M. (2020, March). Study of Various Cloud Service Providers: A Comparative Analysis. In 5th International Conference on Next Generation Computing Technologies (NGCT-2019). Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342. Kumar, D. K. (2022). Classification of Flower Images Using Amazon Rekognition (Doctoral dissertation, KL University). Lal, M., Kumar, K., Arain, R. H., Maitlo, A., Ruk, S. A., & Shaikh, H. (2018). Study of face recognition techniques: A survey. International Journal of Advanced Computer Science and Applications, 9(6). Lazarini, M. A., Rossi, R., & Hirama, K. (2022). A systematic literature review on the accuracy of face recognition algorithms. EAI Endorsed Transactions on Internet of Things, 8(30), e5-e5. Li, Y., & Cha, S. (2019). Face recognition system. arXiv preprint arXiv:1901.02452. Maharani, D. A., Machbub, C., Rusmin, P. H., & Yulianti, L. (2020, December). Improving the capability of real-time face masked recognition using cosine distance. In 2020 6th International conference on interactive digital media (ICIDM) (pp. 1-6). IEEE. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386. Malik, M. I., Wani, S. H., & Rashid, A. (2018). CLOUD COMPUTING- TECHNOLOGIES. International Journal of Advanced Research in Computer Science, 9(2). Mohanta, R. K., & Sethi, B. AMAZON REKOGNITION FOR PATTERN RECOGNITION. Naeem, M., Qureshi, I., & Azam, F. (2015). FACE RECOGNITION TECHNIQUES AND APPROACHES: A SURVEY. Science International, 27(1). Pandey, S., & Sharma, S. (2014). 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