A new descriptor for smile classification based on cascade classifier in unconstrained scenarios
In the development of human–machine interfaces, facial expression analysis has attracted considerable attention, as it provides a natural and efficient way of communication. Congruence between facial and behavioral inference in face processing is considered a serious challenge that needs to be so...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
MDPI AG
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/26686/2/J52%20ODAY%20SYMMETRY-13-00805.PDF http://eprints.utem.edu.my/id/eprint/26686/ https://www.mdpi.com/2073-8994/13/5/805 https://doi.org/10.3390/sym13050805 |
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| Summary: | In the development of human–machine interfaces, facial expression analysis has attracted
considerable attention, as it provides a natural and efficient way of communication. Congruence
between facial and behavioral inference in face processing is considered a serious challenge that needs
to be solved in the near future. Automatic facial expression is a difficult classification issue because
of the high interclass variability caused by the significant interdependence of the environmental
conditions on the face appearance caused by head pose, scale, and illumination occlusions from their
variances. In this paper, an adaptive model for smile classification is suggested that integrates a
row-transform-based feature extraction algorithm and a cascade classifier to increase the precision
of facial recognition. We suggest a histogram-based cascade smile classification method utilizing different facial features. The candidate feature set was designed based on the first order histogram probability, and a cascade classifier with a variety of parameters was used at the classification stage.
Row transformation is used to exclude any unnecessary coefficients in a vector, thereby enhancing the discriminatory capacity of the extracted features and reducing the sophistication of the calculations. Cascading gives the opportunity to train an extremely precise classification by taking a weighted average of poor learners’ decisions. Through accumulating positive and negative images of a single object, this algorithm can build a complete classifier capable of classifying different smiles in a limited amount of time (near real time) and with a high level of precision (92.2–98.8%) as opposed to other algorithms by large margins (5% compared with traditional neural network and 2% compared with Deep Neural Network based methods). |
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