INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303
Robust Human Emotion Analysis Using LBP, GLCM and PNN Classifier 1
S.Seedhana Devi Assistant Professor Department of Information Technology 1 Sri Vidya College of Engineering & Technology Seedhana19@gmail.com
S.Jasmine Rumana2 UG Student Department of Information Technology 2 Sri Vidya College of Engineering & Technology jasminerumana@gmail.com
G.Jayalakshmi3 UG Student Department of Information Technology 3 Sri Vidya College of Engineering & Technology jayalakshmi.tech@gmail.com
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
INTRODUCTION Face Expressions convey high recognizable emotional Signals, their specific forms may have originated not for communication, but as functional adaptations of more direct benefit to the expresser. The common human facial expression deals with happy or angry thoughts, feelings or understanding of the speaker expected or unexpected response from listeners, sympathy, or even what the speaker is conversing with the person opposite to them. The traditional face detection is used to extract face area from an original image. Then to extract eyes, mouth and eyebrow outlines’ position from face area. Face expressions are recognized to have accurate detection. The accuracy can be predicted, if the original face is hidden with duplicate face. If the whole face image is recognized, the performance will be low, so face expressions are chosen. Mostly local features are detected. Beyond the performance, facial expression plays a vital role in communication with sign language. Many geometric approaches are existed for face analysis, which include techniques as linear discriminant analysis (LDA [1], Independent Component analysis (ICA) [3], principal component analysis (PCA) [14],Support Vector Machine (SVM) [4]. Object
recognition based on Gabor wavelet features where global features are detected and classified using SVM classifier [2]. Facial expression illustrates intention, personality, and psychopathology of a person [9]. These methods suffer from the generality problem, which might be extremely different from that of training the face images. To avoid this problem, non-statistical face analysis method using local binary pattern (LBP) has been proposed. Initially, LBP was first introduced by ojala et al [5], which showed a high discriminative power for texture classification due to its invariance to monotonic gray level changes. In existing system, face expressions are recognized using Principle Component Analysis and Geometric methods [3], these methods suffer from Low discriminatory power and high computational load. Geometric features will not provide optimal results. PNN classifier and local binary pattern has been proposed to overcome the problem faced in existing system. PNN classifier has been used widely to identify facial areas with greater discrimination. The significant of above literature has its own limitations to recognize facial expression. To avoid such problems a novel method is derived for facial expression in the present paper. The paper is organized as follows. The overview of the proposed system is presented in section 1. The section 2 represents methods
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