In the field of computer vision research, the study of human behavior is a formidable challenge. The diverse applications of this field, from video surveillance for crowd analysis to healthcare diagnostics, have drawn increasing attention. However, a significant problem persists - the sacrifice of transparency for the sake of predictive accuracy in Artificial Intelligence (AI) solutions. These AI systems often operate as enigmatic black boxes, seemingly conjuring decisions from vast datasets with little to no explanation. The need for clarity and accountability in AI decision-making is paramount as our reliance on these systems continues to grow. Explainable AI Applications for Human Behavior Analysis embarks on a mission to harness AI's innate capability to elucidate upon its own decision-making processes. By focusing on facial expressions, gestures, and body movements, we delve into uncharted territories of research, offering novel methodologies, databases, benchmarks, and algorithms for the analysis of human behavior in natural settings. Geared toward academic scholars, this book compiles the expertise of leading researchers in the field, making it accessible to readers of all educational backgrounds. It bridges the gap between technical depth and ease of comprehension, featuring quick-reference infographics and an array of topics. The aim is not only to inform but also to encourage knowledge sharing among peers. Join us in the exploration of this technology's transformative potential in decoding human behavior, a journey that promotes clarity, innovation, and accountability for our AI-driven future.