Robust Facial Expression Recognition Using Local Binary Patterns and Gabor Filters

Facial expressions and gestures provide intuitional cues for interpersonal communication. Imparting intelligence to computer for identifying facial expressions is a crucial task. Facial expressions and emotions are governed by identification of facial muscle movement by visual cortex and training a machine to identify these highly in-situ movements is our primary interest. This thesis presents robust facial expression analysis algorithms for static images as well as an efficient extension to sequence of images. We present an efficient preprocessing method which eliminates the effect of illumination on the detected face images thus making them efficient for feature extraction. Robust Local Binary Patterns and Gabor filters are implemented for feature extraction which are known to provide efficient face representation and analysis.LBP facial features are represented in form of weighted histograms which are best classified using Kullback Leibler divergence measure .Artificial Neural Network classifier is also tested for classification of fused Gabor and LBP features. Further expressions are rarely defined by static images as their complete essence lies in a sequence of images. So further exploration is concentrated on analyzing expressions from a sequence of images. To eliminate head pose variations in consecutive frames and register images to keep the spatial information intact which is necessary for LBP feature representation we adopted SIFT flow alignment procedure and further tested the resultant image classification with implemented algorithms. The classification accuracy resulted in 95.24% for static expression images and 86.31% for sequence of images which is indeed appreciable when compared to other standard methods

Descriptor feature based on local binary pattern for face classification

Local Binary Patterns (LBP) is a non-parametric descriptor whose purpose is to effectively summarize local image configurations. It has generated increasing interest in many aspects including facial image analysis, vision detection, facial expression analysis, demographic classification, etc. in recent years and has proven useful in various applications. This paper presents a local binary pattern based face recognition (LBP) technology using a Vector Support Machine (SVM). Combine the local characteristics of LBP with universal characteristics so that the general picture characteristics are more robust. To reduce dimension and maximize discrimination, super vector machines (SVM) are used. Screened and Evaluated (FAR), FARR and Accuracy Score (Acc), not only on the Yale Face database but also on the expanded Yale Face Database B datasets, the test results indicate that the approach is accurate and practical, and gives a recognition rate of 98 %

Robust Facial Expression Recognition Using Local Binary Patterns and Gabor Filters

Facial expressions and gestures provide intuitional cues for interpersonal communication. Imparting intelligence to computer for identifying facial expressions is a crucial task. Facial expressions and emotions are governed by identification of facial muscle movement by visual cortex and training a machine to identify these highly in-situ movements is our primary interest. This thesis presents robust facial expression analysis algorithms for static images as well as an efficient extension to sequence of images. We present an efficient preprocessing method which eliminates the effect of illumination on the detected face images thus making them efficient for feature extraction. Robust Local Binary Patterns and Gabor filters are implemented for feature extraction which are known to provide efficient face representation and analysis.LBP facial features are represented in form of weighted histograms which are best classified using Kullback Leibler divergence measure .Artificial Neural Network classifier is also tested for classification of fused Gabor and LBP features. Further expressions are rarely defined by static images as their complete essence lies in a sequence of images. So further exploration is concentrated on analyzing expressions from a sequence of images. To eliminate head pose variations in consecutive frames and register images to keep the spatial information intact which is necessary for LBP feature representation we adopted SIFT flow alignment procedure and further tested the resultant image classification with implemented algorithms. The classification accuracy resulted in 95.24% for static expression images and 86.31% for sequence of images which is indeed appreciable when compared to other standard methods

A Multi-Population FA for Automatic Facial Emotion Recognition

Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models