Robust Human Emotion Analysis Using LBP, GLCM and PNN Classifier
Abstract- The project presents recognition of face expressions based on textural analysis and PNN classifier. Automatic facial expression recognition (FER) plays an important role in HCI systems for measuring people’s emotions by linking expressions to a group of basic emotions such as disgust, sadness, anger, surprise and normal. This approach is another version made to protect the network effectively from hackers and strangers. The recognition system involves face detection, feature extraction and classification through PNN classifier. The face detection module obtains only face images and the face region which have normalized intensity, uniformity in size and shape. The distinct LBP and GLCM are used to extract the texture from face regions to discriminate the illumination changes for texture feature extraction. These features are used to distinguish the maximum number of samples accurately. PNN classifier based on discriminate analysis is used to classify the six different expressions. The simulated results will provide better accuracy and have less algorithmic complexity compared to facial expression recognition approaches.
Index Terms-Distinct Local Binary Pattern (LBP), First Ordered Compressed Image, Gray Level Co-occurrence Matrix (GLCM) and Probabilistic Neural Network (PNN) Classifier, Triangular Pattern
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL