Improving Face Detection Accuracy: A Fusion of Independent Component Analysis and Convolutional Neural Networks

Abstract

Face detection is a fundamental task in computer vision, with applications spanning from security and surveillance to human-computer interaction. This research paper introduces an innovative approach to enhance face detection accuracy by combining Independent Component Analysis (ICA) with Convolutional Neural Networks (CNNs). ICA is employed to extract statistically independent features from facial images, which are then used as inputs for a deep CNN architecture. Experimental results demonstrate the superior performance of this fusion approach compared to traditional methods. This paper discusses the implications of this methodology for real-world applications and its potential to transform the field of computer vision.