Facial expression recognition using distance and shape signature features
| Autor: | Asit Barman, Paramartha Dutta |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Facial expression
business.industry Feature extraction Pattern recognition 02 engineering and technology 01 natural sciences Active appearance model Artificial Intelligence Skewness Salient Multilayer perceptron 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering Kurtosis Entropy (information theory) 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence 010306 general physics business Software Mathematics |
| Zdroj: | Pattern Recognition Letters. 145:254-261 |
| ISSN: | 0167-8655 |
| Popis: | Distance and Shape signature features in human faces offer formidable significance in recognizing facial expressions. Identifying appropriate landmarks is a crucial as well as challenging issue in the field of expression recognition of human faces. Appearance model has been found useful to detect the salient landmarks on human faces. These salient landmarks induce a grid on the human face along with the formation of possible triangles joining the grid. Normalized distance and shape signatures are determined from the grid. Distance signature as well as shape signature find respective stability indices which play important role to recognize the facial expressions. Statistical measures such as range, moment, skewness, kurtosis and entropy are calculated from normalized distance and shape signature pair to supplement the feature set. This enhanced feature set is fed into a Multilayer Perceptron (MLP) to arrive at different expression categories encompassing anger, sadness, fear, disgust, surprise and happy. We investigated our proposed system on Cohn-Kanade (CK+), JAFFE, MMI and MUG databases to conduct and validate our experiment and establish its performance superiority over other existing competitors. |
| Databáze: | OpenAIRE |
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