Performance Comparison of Face Detection Algorithms for Accurate Face Counting
This paper presents a review of the application of machine learning to face detection in images, with a specific focus on face recognition in digital environments. The primary objectives of this research are to develop and analyze advanced machine learning algorithms to improve face identification accuracy. The study provides a detailed evaluation of several key algorithms, including Convolutional Neural Networks (CNNs) and the MTCNN (Multi-Task Cascaded Convolutional Networks). It also benchmarks the performance of established object detection models, such as Histogram of Oriented Gradients (HOG), Cascaded HAAR, and Maximum Margin Object Detection (MMOD). Through empirical evaluation, the MTCNN (Multi-Task Cascaded Convolutional Networks) algorithm demonstrated superior performance, achieving the highest accuracy and most efficient runtime among the tested models.