Facial emotion recognition using average face ratios and fuzzy hamming distance

• Autorzy: Ounachad Khalid , Oualla Mohamed , Sadiq Abdelalim , Souhar Abdelghani

Identyfikatory

DOI

10.14313/JAMRIS/4-2020/43

• Języki publikacji: EN

Abstrakty

EN

Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Nowadays, emotional factors are important as classic functional aspects of customer purchasing behavior. Purchasing choices and decisions making are the result of a careful analysis of the product advantages and disadvantages and of affective and emotional aspects. This paper presents a novel method for human emotion classification and recognition. We generate seven referential faces suitable for each kind of facial emotion based on perfect face ratios and some classical averages. The basic idea is to extract perfect face ratios for emotional face and for each referential face as features and calculate the distance between them by using fuzzy hamming distance. To extract perfect face ratios, we use the point landmarks in the face then sixteen features will be extract. An experimental evaluation demonstrates the satisfactory performance of our approach on WSEFEP dataset. It can be applied with any existing facial emotion dataset. The proposed algorithm will be a competitor of the other proposed relative approaches. The recognition rate reaches more than 90%.

Słowa kluczowe

EN

average face ratios; facial emotion recognition; fuzzy hamming distance; perfect face ratios

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 4
• Strony: 37--44
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Ounachad Khalid

• Department of Informatics, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

autor

Oualla Mohamed

• Department of Informatics, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

autor

Sadiq Abdelalim

• Department of Informatics, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

autor

Souhar Abdelghani

• Department of Informatics, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco

Bibliografia

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•  [9] K. Ounachad, M. Oualla, A. Souhar and A. Sadiq, “Structured learning and prediction in face sketch gender classification and recognition”, International Journal of Computational Vision and Robotics, vol. 10, no. 6, 2020, DOI: 10.1504/IJCVR.2020.110645.
• [10] R. Chellappa, C. L. Wilson and S. Sirohey, “Human and machine recognition of faces: a survey”, Proceedings of the IEEE, vol. 83, no. 5, 1995, 705–741, DOI: 10.1109/5.381842.
• [11] M. Olszanowski, G. Pochwatko, K. Kuklinski, M. Scibor-Rylski, P. Lewinski and R. K. Ohme, “Warsaw set of emotional facial expression pictures: a validation study of facial display photographs”, Frontiers in Psychology, vol. 5, 2015, DOI: 10.3389/fpsyg.2014.01516.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).