Optimizing Facial Detection Using Hybrid HOG-SVM Method

Facial detection plays a pivotal role in computer vision applications, spanning facial recognition, surveillance, and augmented reality. This study delves into the efficacy of the Hybrid method, combining the Histogram of Oriented Gradients (HOG) algorithm with a Support Vector Machine (SVM) classifier for robust facial detection. HOG emphasizes extracting pertinent features, focusing on the shape and texture of objects within images. These features serve as inputs for an SVM classifier, training it to distinguish faces from other objects. The research aims to create a precise facial detection system adaptable to diverse conditions, encompassing variations in orientation, lighting, and shadows. The emphasis lies on optimizing the HOG-SVM approach to achieve exceptional performance without compromising processing efficiency. Experimental evaluations, conducted on the LFW and Wider datasets, demonstrate promising results, showcasing the superior accuracy of our HOG-SVM approach compared to recent methodologies.