Feed Forward Neural Network – Facial Expression Recognition Using 2D Image Texture

Facial Expression Recognition (FER) is a very active field of study in a wide range of fields such as computer vision, human emotional analyses، pattern recognition and AI. FER has received extensive awareness because it can be employed in human computer interaction (HCI), human emotional analyses, interactive video, image indexing and retrieval. Human facial expression Recognition is one of the most powerful and difficult responsibilities of social communication. Face expressions are, in general terms, natural and direct methods of communicating emotions and intentions for human beings. GWT is applied as a preprocess stage. For the classification of face expressions, this study employs the well-known Feed Forward Propagating Algorithm to create and train a neural network

Hybrid Approach for Facial Feature Detection and Tracking under Occlusion

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Hybrid Approach for Facial Feature Detection and Tracking under Occlusion

When a face is partially occluded in an image, the existing discriminative or generative methods often do not find facial features. This is due to the limitations of local facial feature detectors and appearance modeling in discriminative and generative methods, respectively. To solve this problem, we propose a new facial feature detection method that hybridizes the discriminative and generative methods. The proposed method consists of an initialization stage and optimization stage. The initialization stage detects the face, estimates the facial pose, and obtains the initial parameter set by locating the pose-specific mean shape on the detected face. The optimization stage obtains the facial features by updating the parameter set using the combined Hessian matrix and gradient vector of shape and appearance errors obtained from two methods. Further, we extend the proposed facial feature detection to face tracking by adding a template face obtained from the previous image frame. In experiments, the proposed method yields more accurate facial feature detection or tracking under heavy occlusions and pose variations than the existing methods.X11510sciescopu