HIGH PERFORMANCE FACIAL RECOGNITION MATCHER

Year 2023, , 42 - 52, 30.06.2023

Gulsum Akkuzukaya

https://doi.org/10.22531/muglajsci.1218915

Abstract

The utilization of biometric products is an expanding landscape; from general consumers employing it for authenticating into their devices to governments deploying it at the forefront of crime and border control. One sizeable organization required an expansion in their offering within the industryThis study aims to develop a facial matching solution that offers high performance and meets the requirements of the organization’s biometric Subject Matter Experts in order to meet the current gap in the offering. A facial recognition approach known as FaceNet was utilized along with the GO language and MongoDB to produce an application capable of performing enrolments and matches against a persistent set of candidates. This solution was validated against the labeled Faces in the Wild dataset, a challenging set of facial biometric data in function, performance, and accuracy testing. For a subset of 6000 images from the dataset, a 100 % accuracy was recorded from multiple test runs demonstrating no false matches. The application's performance against this subset was averaged over multiple executions using two concurrent connections, which concluded an average enroll response time of 70ms and 236ms for match requests giving transactions per second values of 29 and 8 respectively.

Keywords

face recognition, machine learning, robust, reliable, functionality

References

• Vijayasarathy, L.R., Butler, C.W., “Choice of software development methodologies”. Colorado State University. 2016.
• Hoda, R., Salleh, N., Grundy, J.,. “The rise and evolution of agile software development”. IEEE software 35, 58–6, 2018.
• Almeida, F., Simões, J., . “Moving from waterfall to agile: Perspectives from it portuguese companies”. International Journal of Service Science Management, Engineering, and Technology (IJSSMET) 10, 30–43, 2019.
• Hill, K. “The secretive company that might end privacy as we know it”. In Ethics of Data and Analytics (pp. 170-177), 2020.
• Kortli, Y., Jridi, M., Al Falou, A., and Atri, M.. “Face recognition systems: A survey”. Sensors, 20(2), 342, 2020.
• Lim, K.Y.H., Zheng, P., Chen, C.H., . “A state-of-the-art survey of digital twin: techniques, engineering product lifecycle management and business innovation perspectives”. Journal of Intelligent Manufacturing 31, 1313–1337, 2020.
• Wang, Z., Wang, G., Huang, B., Xiong, Z., Hong, Q., Wu, H., ... and Liang, J. “Masked face recognition dataset and application”. arXiv preprint arXiv:2003.09093, 2020.
• Koubaa, A., Ammar, A., Kanhouch, A., AlHabashi, Y., “Cloud versus edge deployment strategies of real-time face recognition inference.” IEEE Transactions on Network Science and Engineering 9, 143–160, 2021.
• Wang, M., and Deng, W.. “Deep face recognition: A survey.” Neurocomputing, 429, 215-244, 2021.
• Smith, M., and Miller, S.. “The ethical application of biometric facial recognition technology”. Ai and Society, 37(1), 167-175, 2022.
• Almeida, D., Shmarko, K., and Lomas, E. . “The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks”. AI and Ethics, 2(3), 377-387, 2022.
• Zhang, N., and Deng, W. . Fine-grained LFW database. In 2016 International Conference on Biometrics (ICB) (pp. 1-6). IEEE, June, 2016.
• Rao, A. S., and Ganguly, P. Implementation of Efficient Cache Architecture for Performance Improvement in Communication based Systems. In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) (pp. 1192-1195). IEEE, September, 2017.
• Li, D., Dong, M., Yuan, Y., Chen, J., Ota, K., and Tang, Y.. SEER-MCache: A prefetchable memory object caching system for IoT real-time data processing. IEEE Internet of Things Journal, 5(5), 3648-3660, 2018.
• Gallally, J., Marcel, S., and Fiérrez, J. Image quality assessment for fake biometric detection: application to iris, fingerprint and face recognition [J]. IEEE Transactions on Image Processing, 23(2), 710-724, 2014.
• Budiarti, R. P. N., FATHIN, A. N., and Sulistiyani, E. Website-Based Student Achievement Book Using the Waterfall Method. IJRSM: International Journal of Scientific Research and Management, 10(3), 797-808, 2022.