A Comparative Study of Machine Learning and Deep Learning Techniques for Facial Attribute Recognition

Abstract

Facial attribute recognition is the automated process of identifying and classifying human facial characteristics such as gender, age, expression, hair color, eye type, and the presence of accessories like glasses or hats. This paper presents a comprehensive comparative study between classical Machine Learning (ML) approaches—including Support Vector Machines (SVM), Random Forests, and AdaBoost—and modern Deep Learning (DL) architectures such as Convolutional Neural Networks (CNN), VGGNet, ResNet, and Vision Transformers (ViT) for facial attribute recognition. We review the working principles, strengths, and limitations of each approach, and compare them on widely used benchmark datasets including CelebA and LFW. Experimental results indicate that deep learning models significantly outperform traditional ML techniques, achieving up to 96.5% average accuracy on multi-attribute tasks. The paper also discusses challenges such as data imbalance, occlusion, illumination variation, and real-time processing. Finally, we outline future research directions including explainable AI and lightweight model deployment on edge devices.