Semantic-based facial expression recognition using analytical hierarchy process

Authors:
Shyi-Chyi Cheng;Ming-Yao Chen;Hong-Yi Chang;Tzu-Chuan Chou
Affiliations:
Department of Computer Science, National Taiwan Ocean University, Taiwan;Department of Computer and Communication Engineering, National Kaohsiung First University of Science and Technology, Taiwan;Department of Computer and Communication Engineering, National Kaohsiung First University of Science and Technology, Taiwan;Department of Information Management, National Taiwan University of Science and Technology, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

Citing 10
Cited 4

Coding, Analysis, Interpretation, and Recognition of Facial Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying Facial Actions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Software selection: a case study of the application of the analytical hierarchical process to the selection of a multimedia authoring system

Information and Management
Automatic Analysis of Facial Expressions: The State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Facial Expression Recognition Using a State-Based Model of Spatially-Localised Facial Dynamics

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognizing faces with PCA and ICA

Computer Vision and Image Understanding - Special issue on Face recognition
A semantic learning for content-based image retrieval using analytical hierarchy process

Expert Systems with Applications: An International Journal

Selecting the preferable bancassurance alliance strategic by using expert group decision technique

Expert Systems with Applications: An International Journal
Retrieving video features for language acquisition

Expert Systems with Applications: An International Journal
Towards supporting expert evaluation of clustering results using a data mining process model

Information Sciences: an International Journal
Pattern classification and audiovisual content management techniques using hybrid expert systems: A video-assisted bioacoustics application in Abdominal Sounds pattern analysis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

In this paper we present an automatic facial expression recognition system that utilizes a semantic-based learning algorithm using the analytical hierarchy process (AHP). All the automatic facial expression recognition methods are similar in that they first extract some low-level features from the images or video, then these features are used as inputs into a classification system, and the outcome is one of the preselected emotion categories. Although the effectiveness of low-level features in automatic facial expression recognition systems has been widely studied, the success is shadowed by the innate discrepancy between the machine and human perception to the image. The gap between low-level visual features and high-level semantics should be bridged in a proper way in order to construct a seamless automatic facial expression system satisfying the user perception. For this purpose, we use the AHP to provide a systematical way to evaluate the fitness of a semantic description for interpreting the emotion of a face image. A semantic-based learning algorithm is also proposed to adapt the weights of low-level visual features for automatic facial expression recognition. The weights are chosen such that the discrepancy between the facial expression recognition results obtained in terms of low-level features and high-level semantic description is small. In the recognition phase, only the low-level features are used to classify the emotion of an input face image. The proposed semantic learning scheme provides a way to bridge the gap between the high-level semantic concept and the low-level features for automatic facial expression recognition. Experimental results show that the performance of the proposed method is excellent when it is compared with that of traditional facial expression recognition methods.