Comprehensive Analysis of Face Recognition using Machine Learning and Deep Learning Models

Abstract

Face recognition is a cornerstone in computer vision with its applications covering emotion analysis, healthcare and human computer interaction. This paper gives a complete explanation and classification of face recognition models by categorizing into Machine learning (ML), Deep Learning (DL) and hybrid techniques. Traditional methods rely on manual features and statistical evaluations that are relevant, whereas DL methods used convolutional and transformer-based techniques for classification and feature extraction. The integration of both techniques (hybrid) with its benefits performs better flexibility and more consistency for different conditions. This survey presents current innovations such as fuzzy similarity measures, self-distillation methods, segmentation improved Convolutional Neural Network (CNNs) and securely preserving quaternion networks. Applications including masked face detections, light variation enhancement and emotion-aware music recommendation is specified. The comparative analysis of cited works indicates that while DL models achieve superior recognition accuracy (typically >90% on benchmarks like FER-2013), they demand extensive data and computational resources, resulting in higher inference latency compared to hybrid models. Additionally, privacy concerns are an important challenge in multimodal biometric systems. This paper shows research gap in existing methods and demonstrates the demand for face recognition systems that are fast, protect user privacy and adjust with different environments for practical real world scenarios.