Improved multiview biometric object detection for anti spoofing frauds

• Published in: Multimedia tools and applications Vol. 83; no. 33; pp. 80161 - 80177
• Main Authors: Asmitha, P., Rupa, Ch, Nikitha, S., Hemalatha, J., Sahu, Aditya Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: 1238: Recent Advances in Biometrics Based on Biomedical Information; Algorithms; Artificial neural networks; Authentication; Biometric recognition systems; Biometrics; Classrooms; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Deep learning; Human performance; Machine learning; Multimedia Information Systems; Object recognition; Real time; Special Purpose and Application-Based Systems; Spoofing; Theft; RetinaFace; Human face; Authentication; Improved ArcFace; Detection; Recognition; Real-time data
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18458-8

Computer vision and deep learning are essential in human authentication. It provides answers to numerous issues faced in the real world. Moreover, it has a great potential in detecting and recognizing the biometrics. It plays a significant role in reducing frauds such as spoofing, identification (ID) theft, and masking types of issues, which are difficult to perform manually, and many case studies used deep learning algorithms (DLA) like viola-jones, AlexNet, and Tiny YOLO3 but the main limitations of these studies are that they are not capable of giving high accuracy and robustness in the multi-face scenarios. So, in this article, an enhanced ArcFace (Additive Angular Margin loss) referred to as Improved ArcFace (I-AF) utilizes Convolution Neural Network (CNN) as its base architecture for feature extraction and RetinaFace are combined to overcome the above limitation, whereas RetinFace is for detecting and I-AF is for recognizing and authenticating human faces. It gives robust and accurate results while dealing with multi-faces. To evaluate the performance of the human monitoring system, it is implemented on real-time student data in a classroom to track the attendance of individuals. The faces of individuals in a classroom are detected and each detected face will be recognized and finally the result will be mapped for the attendance of individuals. To improve accuracy, the data set termed as labeled data, which contains images of students, is trained using I-AF. The system is 97% accurate, which is better than other methods.