Face Detection and Localization in Video Using HOG with CNN

Faqeda Hassen Kareem^1,*,  Mohammed Abdullah Naser¹

¹ College of Science for Women, University of Babylon, Iraq

Emails: faqeda.albermany.gsci141@student.uobabylon.edu.iq; wsci.mohammed.abud@uobabylon.edu.iq

Abstract

Face detection is important in computer vision and image processing, particularly in surveillance, security systems, video analytics, and facial recognition applications. However, face detection algorithms face challenges like position variations, lighting fluctuations, size and resolution differences, facial expressions, and background clutter. This research aims to develop a system that achieves high accuracy in detecting and localizing faces using local descriptors and spatial feature extraction techniques, specifically the Histogram of Oriented Gradients method (HOG). Using videos from the YouTube Face database, features were extracted from frames and trained using a convolutional neural network (CNN). The HOG technique achieved a 94% accuracy rate and good localization compared to CNN without feature extraction.

Keywords: Face detection; HOG feature extraction; CNN; Euclidean distance