Facial Recognition System Utilizing Real-time Deep Learning Techniques

Year : 2024 | Volume :11 | Issue : 01 | Page : 14-20
By

    V. Suganthi

  1. M. Yogeshwari

  1. Assistant Professor, Department of Computer Science, Chevalier T. Thomas Elizabeth College for Women, Chennai, Tamil Nadu, India
  2. Assistant Professor, Department of Information Technology, School of computing sciences, VISTAS, Chennai, Tamil Nadu, India

Abstract

This research introduces an openly accessible deep learning-based framework designed for facial recognition. The system encompasses five key stages: face segmentation, detection of facial features, face alignment, embedding, and classification. Deep learning methods are employed for the extraction of fiducial points and embedding within the system. For the classification task, a Support Vector Machine (SVM) is utilized due to its efficiency in both training and inference phases. Notably, the system achieves a facial features detection error rate of 0.12103, demonstrating proximity to state-of-the-art algorithms. Additionally, it achieves a remarkable face recognition error rate of 0.05. By integrating advanced deep learning methods for extracting fiducial points and embedding, the system gains enhanced robustness and effectiveness. Importantly, the system is capable of real-time operation, providing timely and efficient facial recognition. This study marks a noteworthy stride in advancing facial recognition systems, demonstrating commendable accuracy and real-time functionality.

Keywords: Facial recognition, advanced learning, facial attributes, neural computing, pattern identification

[This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]

How to cite this article: V. Suganthi, M. Yogeshwari.Facial Recognition System Utilizing Real-time Deep Learning Techniques.Journal of Image Processing & Pattern Recognition Progress.2024; 11(01):14-20.
How to cite this URL: V. Suganthi, M. Yogeshwari , Facial Recognition System Utilizing Real-time Deep Learning Techniques joipprp 2024 {cited 2024 Apr 03};11:14-20. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=138428


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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received February 16, 2024
Accepted March 8, 2024
Published April 3, 2024
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