Abstract
For communication between humans and machines, just like a human-to-human interaction, machines should recognize human facial expressions. A lot of research has been carried out on facial expression recognition in the last two decades, but it is still challenging due to variation in face image parameters like pose variation, different illumination, alignment, occlusion, etc. The recognition accuracy of expressions depends on features extracted and the classifier used. Different features extraction methods reflect different features of a face image. The histogram of the Gradients method is presented in this paper to extract features reflecting edge directions. The experiment was performed on the JAFEE database. The features are computed by dividing face images into cells, and it detects the shape and appearance of a local object by computing the local and edge direction. Support Vector Machine and K-Nearest-Neighbour algorithms are applied for the classification of features. The performance of classifiers is compared with the recognition accuracy and processing time required. It is observed that the recognition accuracy of the K-Nearest Neighbour algorithm is more than the Support Vector Machine and the processing time required is less.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mehrabian: A Communication without words. Psychol. Today. 9(2), 52–55 (1968)
Ekman, P., Friesen , W., Unmasking the face : a guide to recognizing emotions from facial clues (2003)
Uddin, M.Z., Hassan, M.M., Almogren, A., Alamri, A., Alrubaian, M., Fortino, G.: Facial expression recognition utilizing local direction-based robust features and deep belief network. IEEE Access. 5, 4525–4536 (2017)
Sajjad, M., Shah, A., Jan, Z., Shah, S.I., Baik, S.W., Mehmood, I.: Facial appearance and texture feature-based robust facial expression recognition framework for sentiment knowledge discovery. Cluster Comput. 1–19 (2017). https://doi.org/10.1007/s10586-017-0935-z
Kamarol, S.K.A., Jaward, M.H., Kalviainen, H., Parkkinen, J., Parthiban, R.: Joint facial expression recognition and intensity estimation based on weighted votes of image sequences. Pattern Recognit. Lett. 92, 25–32 (2017)
Mistry, K., Zhang, L., Neoh, S.C., Lim, C.P., Fielding, B.: A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Trans. Cybern. 47, 1496–1509 (2017)
Nazir, M., Jan, Z., Sajjad, M.: Facial expression recognition using histogram of oriented gradients based transformed features. Cluster Comput. https://doi.org/10.1007/s10586-017-0921-5
Meena, H.K., Sharma, K.K., Joshi, S.D.: Improved facial expression recognition using graph signal processing, vol. 53, pp. 11–12 (2017)
Arshid, S., Hussain, A., Munir, A., Nawaz, A., Aziz, S.: Multi-stage binary patterns for facial expression recognition in real world. Cluster Comput. (2017). https://doi.org/10.1007/s10586-017-0832-5
Dahmane, M., Meunier, J.: Prototype-based modeling for facial expression analysis. IEEE Trans. Multimed. 16, 1574–1584 (2014)
Iqbal, M.T.B., Abdullah-Al-Wadud, M., Ryu, B., Makhmudkhujaev, F., Chae, O.: Facial expression recognition with neighborhood-aware edge directional pattern (NEDP). IEEE Trans. Affect. Comput. 11(1), 125–137 (2020)
Happy, S.L., Member, S., Routray, A.: Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affect. Comput. 6, 1–12 (2015)
Mollahosseini, A., Hasani, B., Mahoor, M.H.: AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18–31 (2019)
The Japanese Female Facial Expression (JAFFE) Database. http://www.kasrl.org/jaffe.html. Accessed on 30 Mar 2020
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), pp. 511–518 ( 2001)
Ryu, B., Member, S., Kim, J.: Local directional ternary pattern for facial expression recognition. IEEE Trans. Image Process. 26, 6006–6018 (2017)
Song, M., Tao, D., Liu, Z., Li, X., Zhou, M.: Image ratio features for facial expression recognition application. IEEE Trans Syst. Man Cybern. Part B Cybern. 40, 779–788 (2010)
Siddiqi, M.H., Ali, R., Khan, A.M., Park, Y., Lee, S.: Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE Trans. Image Process. 24, 1386–1398 (2015)
Mahmood, A., Hussain, S., Iqbal, K., Elkilani, W.S.: Recognition of facial expressions under varying conditions using dual-feature fusion. Math. Probl. Eng. 2019, 1–12 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patil, S., Patil, Y.M. (2022). Face Expression Recognition Using SVM and KNN Classifier with HOG Features. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_39
Download citation
DOI: https://doi.org/10.1007/978-981-19-2719-5_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2718-8
Online ISBN: 978-981-19-2719-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)