Emotion Recognition

This essential guide dives deep into the science of identifying human emotions through multiple channels, offering a thorough examination of how facial expressions, vocal patterns, and physiological signals like biopotentials reveal our inner states. It systematically unpacks the core research methodology for each modality, moving beyond surface-level analysis to explore the technical processes of feature extraction and classification that power modern emotion recognition systems. The book serves as a crucial resource for understanding the computational frameworks that translate human behavior into quantifiable data, providing a solid foundation in the engineering principles behind affective computing.

What sets this work apart is its dedicated focus on the critical engineering challenges of feature selection, dimensionality reduction, and multi-modal fusion—the very techniques that improve accuracy in real-world applications. Readers interested in artificial intelligence, human-computer interaction, and psychological computing will find particularly valuable insights into how different emotional cues can be combined for more robust recognition systems. The practical emphasis on classifier design and signal processing makes this especially relevant for developers building emotion-aware technologies, from mental health apps to advanced user interfaces that respond to human affect.