A novel approach to facial recognition utilizing hidden Markov models in computer vision employing Python

Abstract

Image processing today is more commonly referred to as digital image processing, which is a branch of computer science that deals with the processing of digital signals representing images captured by digital cameras or scanned by scanners. In its specific sense, image processing refers to any form of signal processing where the input is an image. The face plays a fundamental role in the identification of individuals and the expression of their emotions within society. Human capability in face recognition is remarkable; we can identify thousands of faces learned throughout our lifetimes at a glance. The CMPA framework is applied to experiments that involved part of a face recognition competition. Analyses indicate that for matching frontal faces in static images, algorithms consistently outperform humans. In the case of video and pairs of static faces, humans are superior. Ultimately, based on the CMPA framework, we proposed a performance index for face recognition, presenting a competitive issue for algorithms that exceed human capabilities in general face recognition tasks. The HMM method relies on matching image templates to a chain of states in a doubly hidden stochastic model. This section addresses the core principles of HMM and describes how to utilize it for face recognition by evaluating training data extraction and the resulting features. It is observed that each segment presents a feature (nose, eye, forehead, etc.). The use of hidden Markov model significantly improves identification rates.