Feature Based Techniques for a Face Recognition using Supervised Learning Algorithms based on Fixed Monocular Video Camera

  • Isra Anwar University of Management and Technology, Lahore, Pakistan
  • Syed Farooq Ali University of Management and Technology, Lahore, Pakistan
  • Jameel Ahmad University of Management and Technology, Lahore, Pakistan
  • Sumaira Zafar University of Management and Technology, Lahore, Pakistan
  • Malik Tahir Hassan University of Management and Technology, Lahore, Pakistan
Keywords: face identification, face recognition, geometric features, supervised learning, Support Vector Machine (SVM)


Abstract Views: 69

Automatic face recognition has ample significance in biometric research. Recent decades have witnessed enormous growth in this research area. Face-based identification is always considered more expedient as compared to other biometric authentications owing to its uniqueness and wide acceptance. The major contribution of this work is twofold; firstly, it comprises an extension of manual thresholding feature-based face recognition approach to an automatic feature-based supervised learning face recognition. Secondly, various new feature sets are proposed and tested on several classifiers for 2, 3, 4, and 5 persons. In addition, the use of slope features of facial components, such as the nose, right eye, left eye, and lips along with other conventional features for face recognition is also a unique contribution of this research. Multiple experiments were performed on the UMT face database. The results demonstrated a comparison of 5 different sets of feature-based approaches on 7 classifiers using the metrics of time efficiency and accuracy. They also depicted that the proposed approaches achieve a percentage accuracy of up to 95.5%.


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How to Cite
Isra Anwar, Syed Farooq Ali, Ahmad, J., Sumaira Zafar, & Malik Tahir Hassan. (2022). Feature Based Techniques for a Face Recognition using Supervised Learning Algorithms based on Fixed Monocular Video Camera. Innovative Computing Review, 2(2). https://doi.org/10.32350/icr.0202.05