Expression recognition using fuzzy spatio-temporal modeling
Pattern Recognition
Facial expression recognition based on shape and texture
Pattern Recognition
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Human facial expression recognition using hybrid network of PCA and RBFN
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
ANN face detection with skin color distribution rules
Machine Graphics & Vision International Journal
Facial expression recognition based on cortex-like mechanisms
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Facial emotion recognition with expression energy
Proceedings of the 14th ACM international conference on Multimodal interaction
Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks
International Journal of Mobile Computing and Multimedia Communications
A neural-AdaBoost based facial expression recognition system
Expert Systems with Applications: An International Journal
An SVM-AdaBoost facial expression recognition system
Applied Intelligence
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A new technique for facial expression recognition is proposed, which uses the two-dimensional (2D) discrete cosine transform (DCT) over the entire face image as a feature detector and a constructive one-hidden-layer feedforward neural network as a facial expression classifier. An input-side pruning technique, proposed previously by the authors, is also incorporated into the constructive learning process to reduce the network size without sacrificing the performance of the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having five facial expression images (neutral, smile, anger, sadness, and surprise). Images of 40 men are used for network training, and the remaining images of 20 men are used for generalization and testing. Confusion matrices calculated in both network training and generalization for four facial expressions (smile, anger, sadness, and surprise) are used to evaluate the performance of the trained network. It is demonstrated that the best recognition rates are 100% and 93.75% (without rejection), for the training and generalizing images, respectively. Furthermore, the input-side weights of the constructed network are reduced by approximately 30% using our pruning method. In comparison with the fixed structure back propagation-based recognition methods in the literature, the proposed technique constructs one-hidden-layer feedforward neural network with fewer number of hidden units and weights, while simultaneously provide improved generalization and recognition performance capabilities.