Automatic facial feature extraction and expression recognition based on neural network
This addresses facial expression recognition for emotion analysis, but it is incremental as it applies existing methods like neural networks and edge detection to a standard dataset.
The paper tackles automatic facial feature extraction and expression recognition from frontal images, achieving 100% accuracy on the training set and 95.26% on the test set using a neural network classifier.
In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.