Facial Feature Extraction Review for Contemporary applications
Gaganjot Singh, Shinepreet Kaur, Shivali Devi · 2024
Recognizing a face is an intricate cognitive process that showcases the remarkable capabilities of the human brain in visual perception, a phenomenon deeply rooted in evolutionary biology. In an attempt to emulate this complex phenomenon, computer vision technology has been developed to serve as a sophisticated tool capable of recognizing and distinguishing faces, akin to the human visual system, particularly in the domain of biometric identification methods employed for verifying individuals’ identities. This is done by identifying the unique features of a human’s face through various algorithms. This paper reviews the efficacy of popular approaches to portraying facial features in face pictures. Geometrical-based methods, template-based methods, and appearance-based methods were traditional ways for extracting facial features before the onset of Machine learning for this application. Accuracy through confusion matrix have been comprehended and traditional methods have been found more fit for static and atomic systems although, machine learning algorithms give better and consistent results at the cost of developing and maintaining the computational system underneath.