Automatic 3D facial expression recognition using geometric and textured feature fusion

3D facial expression recognition has gained more and more interests from affective computing society due to issues such as pose variations and illumination changes caused by 2D imaging having been eliminated. There are many applications that can benefit from this research, such as medical applications involving the detection of pain and psychological effects in patients, in human-computer interaction tasks that intelligent systems use in today's world. In this paper, we look into 3D Facial Expression Recognition, by investigating many feature extraction methods used on the 2D textured images and 3D geometric data, fusing the 2 domains to increase the overall performance. A One Vs All Multi-class SVM Classifier has been adopted to recognize the expressions Angry, Disgust, Fear, Happy, Neutral, Sad and Surprise from the BU-3DFE and Bosphorus databases. The proposed approach displays an increase in performance when the features are fused together

Histogram of Oriented Phase (HOP): A New Descriptor Based on Phase Congruency

In this paper we present a low level image descriptor called Histogram of Oriented Phase based on phase congruency concept and the Principal Component Analysis (PCA). Since the phase of the signal conveys more information regarding signal structure than the magnitude, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the image with respect to its neighborhood. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the Histogram of Oriented Phase (HOP) features. The dimensionality of HOP features is reduced using PCA algorithm to form HOP-PCA descriptor. The dimensionless quantity of the phase congruency leads the HOP-PCA descriptor to be more robust to the image scale variations as well as contrast and illumination changes. Several experiments were performed using INRIA and DaimlerChrysler datasets to evaluate the performance of the HOP-PCA descriptor. The experimental results show that the proposed descriptor has better detection performance and less error rates than a set of the state of the art feature extraction methodologies

Power LBP: A Novel Texture Operator for Smiling and Neutral Facial Display Classification

AbstractTexture operators are commonly used to describe image content for many purposes. Recently they found its application in the task of emotion recognition, especially using local binary patterns method, LBP. This paper introduces a novel texture operator called power LBP, which defines a new ordering schema based on absolute intensity differences. Its definition as well as interpretation are given.The performance of suggested solution is evaluated on the problem of smiling and neutral facial display recognition. In order to evaluate the power LBP operator accuracy, its discriminative capacity is compared to several members of the LBP family. Moreover, the influence of applied classification approach is also considered, by presenting results for k-nearest neighbour, support vector machine, and template matching classifiers. Furthermore, results for several databases are compared

The Performance of LBP and NSVC Combination Applied to Face Classification

The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a new fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the new classifier Neighboring Support Vector Classifier (NSVC) for classification. The experimental results on different natural images show that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC exceeds 99%

Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin