Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face and Gesture Recognition: Overview
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Learning features for fingerprint classification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Gabor wavelet representation for 3-D object recognition
IEEE Transactions on Image Processing
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Generative learning of visual concepts using multiobjective genetic programming
Pattern Recognition Letters
A psychologically-inspired match-score fusion mode for video-based facial expression recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Feature selection for improved 3D facial expression recognition
Pattern Recognition Letters
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Feature extraction is one of the key steps in object recognition. In this paper we propose a novel genetically inspired learning method for facial expression recognition (FER). Unlike current research on facial expression recognition that generally selects visually meaningful feature by hands, our learning method can discover the features automatically in a genetic programming-based approach that uses Gabor wavelet representation for primitive features and linear/nonlinear operators to synthesize new features. These new features are used to train a support vector machine classifier that is used for recognizing the facial expressions. The learned operator and classifier are used on unseen testing images. To make use of random nature of a genetic program, we design a multi-agent scheme to boost the performance. We compare the performance of our approach with several approaches in the literature and show that our approach can perform the task of facial expression recognition effectively.