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JAIT 2024 Vol.15(1): 118-126
doi: 10.12720/jait.15.1.118-126

Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

Krishnaraj M. * and Jeberson Retna Raj R.
Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu 600119, India
Email: monykrishnaraj@gmail.com (K.M.); jebersonretnarajr@gmail.com (J.R.R.R.)
*Corresponding author

Manuscript received February 27, 2023; revised June 28, 2023; accepted July 31, 2023; published January 18, 2024.

Abstract—Face recognition technology is widely used for access control, security, identification, safeguarding, verification, timekeeping, and machine vision, etc. a new face identification algorithm referred to as Multi-Task Cascaded Convolutional Network (MTCCN) has emerged and has been widely used in high accuracy and efficiency in facial recognition, active facial patch identification framework face detection, selection of eyes, nose, lip, and eyebrow, identifying facial patches location and extraction of patches. This paper aims to discuss the recognition and identification of faces using layers of the Convolutional Neural Network (CNN). It is done to process camera frames as they appear and subsequent identification of the person. With three convolutional networks, MTCCN outperforms many face detection tests incredibly well, even though it maintains real-time performance. An active facial patch using MTCNN method is introduced for recognizing human faces in real time was developed, evaluated, and 97.62% of the time, the technique could recognize human faces correctly.
Keywords—face identification, Multi-Task Cascaded Convolutional Network (MTCCN), active facial patches, convolutional networks, classification, region of interest

Cite: Krishnaraj M. and Jeberson Retna Raj R., "Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks," Journal of Advances in Information Technology, Vol. 15, No. 1, pp. 118-126, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.