A pipeline that detects sketch expressions with CNNs on FACS Action Units, transfers them to the Valley Girl parametric model, and refines alignment with Active Snake Contours.
Automatic facial feature extraction and expression recognition based on neural network
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abstract
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.
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Generating 3D models from sketches of human faces using a combined approach of Convolutional Neural Networks, Procedural Modeling, and Contour Mapping
A pipeline that detects sketch expressions with CNNs on FACS Action Units, transfers them to the Valley Girl parametric model, and refines alignment with Active Snake Contours.