Facial Emotion Profiling Based on Emotion Specific Feature Model

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• 关键词：Facial emotion recognition ; Measurement vector ; Fuzzy reasoning model ; Recognition accuracy
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9492
• 期：1
• 页码：556-565
• 全文大小：1,264 KB
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• 作者单位：Md. Nazrul Islam (17)
  Chu Kiong Loo (17)
  
  17. Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya, Lembah Pantai, 50603, Kuala Lumpur, Malaysia
  
• 丛书名：Neural Information Processing
• ISBN：978-3-319-26561-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Facial emotion profiling is rapidly becoming an area of intense interest in machine vision society for decade. In spite of major efforts, there are several open questions on how to embed the emotional intelligence in machine to respond immediately and precisely over facial expressions. In this sense, this paper presents an automatic facial emotion profiling from emotion specific feature model. A 17-point feature model on the frontal face region is proposed to track per frame facial emotion robustly. A measurement vector is formed based on a set of geometric distance displacements of a pair of feature points between neutral and expressive face frame. A two-stage fuzzy reasoning model is proposed to classify universal facial expressions. In the first stage measurements are fuzzified and mapped onto an Action Units (AUs) and later AUs are fuzzified and mapped onto an Emotion in the second-stage of fuzzy reasoning model. The overall performance of the proposed system is evaluated on two publicly available facial expression databases, average emotion recognition accuracy of 91 % was achieved for RaFD and 94 % for CK + database.