[en] Facial recognition has been one of the most intriguing and exciting research topics over the last few years. It involves multiple face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition. However, all of these algorithms are derived from large deep-learning architectures, leading to limitations in development, scalability, accuracy, and deployment for public use with mere CPU servers. Also, large data sets that contain hundreds of thousands of records are often required for training purposes. In this paper, we propose a complete pipeline for an effective face-recognition application that requires only a small dataset of Vietnamese celebrities and a CPU for training, solving the problem of data leakage, and the need for GPU devices.The pipeline is based on the combination of a conversion algorithm from face vectors to string tokens and the indexing & retrieval process by Elasticsearch, thereby tackling the problem of online learning in facial recognition. Compared with other popular algorithms on the same data set, our proposed pipeline not only outperforms the counterpart in terms of accuracy but also delivers faster inference, which is essential to real-time applications.
Disciplines :
Computer science
Author, co-author :
NGUYEN, van Dat ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
NGUYEN, Trung Son; Sun Asterisk VN
PHAM, Thi Hong Anh; Sun Asterisk VN
PHAM, Van Toan; Sun Asterisk VN
HOANG, Thu Thao; Sun Asterisk VN
TA, Minh Thanh; Le Quy Don Technical University
External co-authors :
yes
Language :
English
Title :
HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM
Publication date :
10 March 2023
Journal title :
Computer Science
ISSN :
1508-2806
eISSN :
2300-7036
Publisher :
AGH University of Science and Technology Press, Poland
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