The paper "Toward an Affect-Sensitive Multimodal Human-Computer Interaction" by MAJA PANTIC and LEON J. M. ROTHKRANTZ explores the importance of recognizing affective states in human-computer interaction (HCI) to enhance its human-like, effective, and efficient nature. Emotional intelligence, which includes the ability to recognize and interpret affective states, is crucial for successful interpersonal interactions. The authors argue that next-generation HCI should incorporate this capability to improve user experience. The paper surveys past work in developing automated systems to recognize affective states, focusing on nonverbal communicative cues such as facial expressions, body movements, and vocal and physiological reactions. It highlights the challenges in designing such systems, including the complexity of human perception and the need for multimodal integration. Key issues discussed include: 1. **What is an affective state?** The nature of affective states and how they are described by automatic systems. 2. **What evidence supports affective states?** Which nonverbal signals convey affective arousal. 3. **How to combine evidence to recognize affective states?** The integration of multiple sensory modalities and the handling of temporal information. The paper also outlines a taxonomy of the problem domain, emphasizing the importance of multimodal integration, multisensory information fusion, and context sensitivity. It reviews current state-of-the-art techniques for recognizing affective states from facial images and audio signals, noting the limitations and future directions. The authors conclude by emphasizing the need for a common testing resource to evaluate the performance of affective recognition systems and the importance of addressing the challenges in developing robust and accurate affective analyzers.The paper "Toward an Affect-Sensitive Multimodal Human-Computer Interaction" by MAJA PANTIC and LEON J. M. ROTHKRANTZ explores the importance of recognizing affective states in human-computer interaction (HCI) to enhance its human-like, effective, and efficient nature. Emotional intelligence, which includes the ability to recognize and interpret affective states, is crucial for successful interpersonal interactions. The authors argue that next-generation HCI should incorporate this capability to improve user experience. The paper surveys past work in developing automated systems to recognize affective states, focusing on nonverbal communicative cues such as facial expressions, body movements, and vocal and physiological reactions. It highlights the challenges in designing such systems, including the complexity of human perception and the need for multimodal integration. Key issues discussed include: 1. **What is an affective state?** The nature of affective states and how they are described by automatic systems. 2. **What evidence supports affective states?** Which nonverbal signals convey affective arousal. 3. **How to combine evidence to recognize affective states?** The integration of multiple sensory modalities and the handling of temporal information. The paper also outlines a taxonomy of the problem domain, emphasizing the importance of multimodal integration, multisensory information fusion, and context sensitivity. It reviews current state-of-the-art techniques for recognizing affective states from facial images and audio signals, noting the limitations and future directions. The authors conclude by emphasizing the need for a common testing resource to evaluate the performance of affective recognition systems and the importance of addressing the challenges in developing robust and accurate affective analyzers.