A Statistical and Machine Learning Based Face Identification System with Enhanced Multiple Weighted Facial Attribute Sets


  • Shreyas Patel, Avinash Chaudhari, Pradeep Gamit, Aniruddhsinh Dodiya


Face Recognition, Multiple Weighted Facial Attribute, Principal Component Analysis


Academic and business institutions alike have been more interested in facial recognition studies in recent years. The idea of face recognition has grown in significance in several applications due to its openness and the myriad of security characteristics it encompasses. Face recognition solves several issues with alignment, age, lighting, emotion, and lighting. The aforementioned challenge arose while trying to differentiate one face from another in a facial recognition system. This study proposes a novel method for improving face recognition performance using Multiple Weighted Facial Attribute Sets in conjunction with the Principal Component Analysis (PCA) methodology. The results of this study demonstrate that the recognition system's overall performance was affected by the weights assigned to the various qualities. During the matching process, the user-defined input component of the proposed approach will prioritise a collection of picture characteristics.


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How to Cite

Shreyas Patel,. (2024). A Statistical and Machine Learning Based Face Identification System with Enhanced Multiple Weighted Facial Attribute Sets. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1743–1751. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5745



Research Article