Facial & Micro-Expression Recognition Using CNNs

Facial & Micro-Expression Recognition Using CNNs explores the application of deep learning and computer vision for recognizing human emotions from facial expressions. The book presents a comprehensive CNN-based framework for identifying emotions such as happiness, sadness, anger, fear, surprise, disgust, and neutrality under challenging conditions including variations in illumination, pose, occlusion, age, and demographics.It covers modern deep learning architectures, including VGG16, ResNet, DenseNet, and STCNN, along with preprocessing, data augmentation, and feature-extraction techniques such as LBP, Gabor filters, and Haar-like features. Benchmark datasets such as FER2013 and CK+ are discussed to evaluate recognition performance.The book also examines micro-expressions, attention-based networks, ensemble learning, feature fusion, and spatio-temporal analysis for improving recognition accuracy. Particular emphasis is placed on real-time applications, robustness, fairness, and ethical considerations, making this work relevant to researchers, students, and professionals in artificial intelligence, computer vision, affective computing, and human-computer interaction.