Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Face Recognition Using a Fuzzy-Gaussian Neural Network
ICCI '02 Proceedings of the 1st IEEE International Conference on Cognitive Informatics
SVM-based Nonparametric Discriminant Analysis, An Application to Face Detection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Journal of Cognitive Neuroscience
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This paper presents a methodology to identifying a facial expression of human being based on the information theory approach of coding. This task is consists of two major phases: Extraction of appropriate facial features and consequent recognition of the user's emotional state that can be robust to facial expression variations among different users is the topic of this paper. 1) Identifying maximum matching face from database. 2) Extracting a facial expression from matched image. First phase consist of feature extraction using Principal Component analysis & face recognition using feed forward back propagation Neural Network with the use of eigen vector for calculating eigen values of images. The architecture considered for implementation is a Neuro-Fuzzy system with concepts of artificial intelligence. The approach chosen for the implementation is Soft Computing which is basically a synergistic integration computing paradigms: neural networks, fuzzy logic to provide a flexible framework to construct computationally intelligent systems. A Evolving Emotional Intelligence may contains simulated emotions, including sadness, joy, anger, fear, hope, relief, disappointment, gratitude, pride, shame, love and hate. Research on human psychology had long considered the notion of an emotion (e.g., happy) to be a matter of degree; however, Using fuzzy modeling proved to produce a more representative picture of the emotional process. By testing the above task over 1000 to 10000 images including both color, grayscale images of same & different human faces & may get the 80 to 90% accurate result.