Expression Recognition In Image Sequences Research Articles

Features classification using geometrical deformation feature vector of support vector machine and active appearance algorithm for automatic facial expression recognition

This paper proposes a method for facial expression recognition in image sequences. Face is detected from the scene and then facial features are detected using image normalization, and thresholding techniques. Using an optimization algorithm the Candide wire frame model is adapted properly on the first frame of face image sequence. In the subsequent frames of image sequence facial features are tracked using active appearance algorithm. Once the model fits on the first frame, animation parameters of model are set to zero, to obtain the shape of model for the neutral facial expression of the same face. The last frame of the image sequence corresponds to greatest facial expression intensity. The geometrical displacement of the Candide wire frame nodes, between the neutral expression frame and the last frame, is used as an input to the multiclass support vector machine, which classifies facial expression into one of the class such as happy, surprise, sadness, anger, disgust, fear and neutral. This method is applicable for frontal as well as tilted faces with angle $$\pm 30\,^{\circ }, \pm 45\,^{\circ }, \pm 60\,^{\circ }$$ ± 30 ? , ± 45 ? , ± 60 ? with respect to y axis.

Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

Features classification using support vector machine for a facial expression recognition system

A methodology for automatic facial expression recognition in image sequences is proposed, which makes use of the Candide wire frame model and an active appearance algorithm for tracking, and support vector machine (SVM) for classification. A face is detected automatically from the given image sequence and by adapting the Candide wire frame model properly on the first frame of face image sequence, facial features in the subsequent frames are tracked using an active appearance algorithm. The algorithm adapts the Candide wire frame model to the face in each of the frames and then automatically tracks the grid in consecutive video frames over time. We require that first frame of the image sequence corresponds to the neutral facial expression, while the last frame of the image sequence corresponds to greatest intensity of facial expression. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the SVM, which classify the facial expression into one of the classes viz happy, surprise, sadness, anger, disgust, and fear.

Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines

In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.