Autors: Manolova, A. H., Neshov, N. N., Panev, S. S., Tonchev, K. T.
Title: Facial expression classification using supervised descent method combined with PCA and SVM
Keywords: Supervised Descent Method - SVM, PCA, Facial expression, Emotion recognition

Abstract: It has been well known that there is a correlation between facial expression and person’s internal emotional state. In this paper we use an approach to distinguish between neutral and some other expression: based on the displacement of important facial points (coordinates of edges of the mouth, eyes, eyebrows, etc.). Further the feature vectors are formed by concatenating the landmarks data from Supervised Descent Method, applying PCA and use these data as an input to Support Vector Machine (SVM) classifier. The experimental results show improvement of the recognition rate in comparison to some state-of-the-art facial expression recognition techniques.

References

    Issue

    1st International Workshop on Biometrics, BIOMET 2014, vol. 8897, pp. 165-175, 2014, Bulgaria, Springer Verlag, DOI 10.1007/978-3-319-13386-7_13

    Copyright Springer Verlag

    Цитирания (Citation/s):
    1. Wang, S., Gong, F., Tian, J., Jin, G., Han, S., Identification algorithm of electrical equipment type and fault state based on FastPCA and SVM, Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 8729456, pp. 33-37 - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Li, Z., Jiang, X., Pang, Y., Evaluation of Face Recognition Techniques Based on Symlet 2 Wavelet and Support Vector Machine, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11637 LNCS, 2019, pp. 228-239 - 2019 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в национален форум с межд. уч., публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science