Automatic facial expression analysis aims to replicate human ability to detect and interpret facial expressions in scenes. This paper reviews past work on three key problems: face detection, facial expression information extraction, and classification. It discusses the human visual system's capabilities and proposes an ideal automated system for facial expression analysis. The system should perform these tasks automatically, with robustness to varying lighting, occlusions, and head movements. It should also classify expressions into emotional categories, such as happiness, sadness, surprise, fear, anger, and disgust, as defined by Ekman. The paper surveys recent techniques for facial expression analysis, including holistic and analytic approaches, and discusses challenges in automated classification, such as blending emotions and contextual interpretation. It also highlights the importance of real-time processing for multimodal interfaces and the need for adaptable systems that can handle diverse subjects. The paper concludes with a summary of current methods and future research directions in facial expression analysis.Automatic facial expression analysis aims to replicate human ability to detect and interpret facial expressions in scenes. This paper reviews past work on three key problems: face detection, facial expression information extraction, and classification. It discusses the human visual system's capabilities and proposes an ideal automated system for facial expression analysis. The system should perform these tasks automatically, with robustness to varying lighting, occlusions, and head movements. It should also classify expressions into emotional categories, such as happiness, sadness, surprise, fear, anger, and disgust, as defined by Ekman. The paper surveys recent techniques for facial expression analysis, including holistic and analytic approaches, and discusses challenges in automated classification, such as blending emotions and contextual interpretation. It also highlights the importance of real-time processing for multimodal interfaces and the need for adaptable systems that can handle diverse subjects. The paper concludes with a summary of current methods and future research directions in facial expression analysis.