{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:30:34Z","timestamp":1772119834696,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In the development of human\u2013machine 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\u2019 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\u201398.8%) as opposed to other algorithms by large margins (5% compared with traditional neural network and 2% compared with Deep Neural Network based methods).<\/jats:p>","DOI":"10.3390\/sym13050805","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T22:51:42Z","timestamp":1620255102000},"page":"805","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A New Descriptor for Smile Classification Based on Cascade Classifier in Unconstrained Scenarios"],"prefix":"10.3390","volume":"13","author":[{"given":"Oday A.","family":"Hassen","sequence":"first","affiliation":[{"name":"Department of Information Technology, University Technical Malaysia Melaka, Hang Taya, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nur Azman","family":"Abu","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University Technical Malaysia Melaka, Hang Taya, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaheera Zainal","family":"Abidin","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University Technical Malaysia Melaka, Hang Taya, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2723-1549","authenticated-orcid":false,"given":"Saad M.","family":"Darwish","sequence":"additional","affiliation":[{"name":"Institute of Graduate Studies and Research, University of Alexandria, 163 Horreya Avenue, El Shatby 21526, Alexandria P.O. Box 832, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Andrian, R., and Supangkat, S.H. (2020, January 19\u201320). Comparative Analysis of Deep Convolutional Neural Networks Architecture in Facial Expression Recognition: A Survey. Proceedings of the International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia.","DOI":"10.1109\/ICISS50791.2020.9307567"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1111\/jocd.13569","article-title":"Masking our emotions: Botulinum toxin, facial expression, and well-being in the age of COVID-19","volume":"19","author":"Nestor","year":"2020","journal-title":"J. Cosmet. 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