Structured Learning and Prediction in Facial Emotion Classification and Recognition
Khalid Ounachad1, Mohamed Oualla2, Abdelghani Souhar3, Abdelalim Sadiq4
1Ounachad*, Ibn Tofail University, Faculty of sciences, Kenitra, Morocco. email@example.com Oualla, Faculty of sciences and technology, Moulay Ismail University, Errachidia, Morocco.
2Souhar, Ibn Tofail University, Faculty of sciences, Kenitra, Morocco.
3Sadiq, Ibn, Tofail University, Faculty of sciences, Kenitra, Morocco.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 152-160 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6421029320/2020©BEIESP | DOI: 10.35940/ijeat.C6421.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Structured prediction methods have become, in recent years, an attractive tool for many machine-learning applications especially in the image processing area as in customers satisfaction prediction by using facial recognition systems, in criminal investigations based on face sketches recognition, in aid to autistic children and so. The main objective of this paper is the identification of the emotion of the human being, based on their facial expressions, by applying structured learning and perfect face ratios. The basic idea of our approach is to extract the perfect face ratios from a facial emotion image as the features, this face emotional images are labeled with their kind of emotions (the seven emotions defined in literature). For this end, first we determined sixty-eight landmarks point of image faces, next we applied a new deep geometric descriptor to calculate sixteen features representing the emotional face. The training and the testing tasks are applied to the Warsaw dataset: The Set of Emotional Facial Expression Pictures (WSEFEP) dataset. Our proposed approach can be also applied in others competitor facial emotion datasets. Based on experiments, the evaluation demonstrates the satisfactory performance of our applied method, the recognition rate reaches more than 97% for all seven emotions studied and it exceeds 99.20% for neutral facial images.
Keywords: Supervised learning, Structured Learning, prediction, Facial Emotion Recognition, Perfect Face Ratios, Emotional Facial Expression, WSEFEP dataset.