Emotion Recognition: A Pattern Analysis Approach (Hardcover)

A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals

This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers.

Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability.

There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems.

Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book

• Offers both foundations and advances on emotion recognition in a single volume
• Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains
• Inspires young researchers to prepare themselves for their own research
• Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

這本書提供了關於情緒辨識的基礎和當前研究方向，包括面部表情、聲音、手勢和生物電位信號。書中詳細探討了不同情緒辨識模式的研究方法論，重點討論了基於面部表情、聲音和生物電位信號的情緒辨識。特別強調了特徵選擇、特徵降維、分類器設計和多模態融合等方法，以提高情緒分類器的性能。

本書由多位專家撰寫，介紹了多種工具和技術，包括動態貝葉斯網絡、神經網絡、隱馬爾可夫模型、粗糙集、二階模糊集、支持向量機等在不同模式下的情緒辨識應用。書末還討論了在汽車領域中的情緒辨識，以確定駕駛員的壓力和憤怒，這些情緒可能會降低他們的表現和駕駛能力。

情緒辨識在心理治療、生物醫學和政府、公共和私營機構的安全等各個領域中需求日益增加。情緒辨識的重要性已經被包括惠普在內的行業給予了優先考慮，並在設計和開發下一代人機界面（HCI）系統中得到應用。

《情緒辨識：一種模式分析方法》對研究人員、研究生和從業人員非常有興趣，因為本書：

- 在一本書中提供了情緒辨識的基礎和進展
- 利用不同領域的計算工具，提供了深入而有見地的介紹
- 激勵年輕研究人員為自己的研究做好準備
- 通過新技術（如微軟Kinect、腦電圖系統等）展示了未來研究的方向。