Book

Description

Face recognition has quietly moved from science fiction to silent infrastructure — embedded in the devices we carry, the borders we cross, and the cities we inhabit. Its applications are reshaping industries at an unprecedented pace. Yet behind every accurate identification lies decades of algorithmic innovation, dataset engineering, and unsolved challenges. The Evolution of Face Recognition offers a comprehensive and authoritative journey through the full landscape of this field. The book systematically examines the complete face recognition pipeline — from preprocessing and face detection to alignment, anti-spoofing, feature extraction, and final matching. It surveys four major categories of feature extraction: geometric methods, appearance-based approaches, local texture methods, and state-of-the-art deep learning architectures. Readers will find detailed analyses of landmark models — from Eigenfaces and Fisherfaces to DeepFace, FaceNet, ArcFace, and beyond — alongside a thorough exploration of innovative loss functions that drive modern recognition accuracy. The book also introduces 14 widely used datasets and presents systematic performance comparisons across algorithms and benchmarks. Crucially, this work critically engages with the field's open challenges: pose and illumination variation, occlusion, aging, face spoofing, and pressing ethical concerns including racial and gender bias, and privacy. It bridges theoretical foundations with real-world deployment considerations, making it an indispensable reference for researchers, engineers, and practitioners in computer vision and artificial intelligence.