Comparison Of LBPH, Fisherface, and PCA For Facial Expression Recognition of Kindergarten Student

Main Article Content

Muhammad Furqan Rasyid


Face recognition is the biometric personal identification that gaining a lot of attention recently. An increasing need for fast and accurate face expression recognition systems. Facial expression recognition is a system used to identify what expression is displayed by someone. In general, research on facial expression recognition only focuses on adult facial expressions. The introduction of human facial expressions is one of the very fields of research important because it is a blend of feelings and computer applications such as interactions between humans and computers, compressing data, face animation and face image search from a video. This research process recognizes facial expressions for toddlers, precisely for kindergarten students. But before making this research system Comparing three methods namely PCA, Fisherface and LBPH by adopts our new database that contains the face of individuals with a variety of pose and expression. which will be used for facial expression recognition. Fisherface accuracy was obtained at 94%, LBPH 100%, and PCA 48.75%.

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How to Cite
Rasyid, M. F. (2022). Comparison Of LBPH, Fisherface, and PCA For Facial Expression Recognition of Kindergarten Student. International Journal Education and Computer Studies (IJECS), 2(1), 19–26.
Author Biography

Muhammad Furqan Rasyid, Universitas Dipa Makassar

Informatics Management, Universitas Dipa Makassar, Indonesia


Kudiri, K.M., Said, A.M. and Nayan, M.Y., 2012, June. Emotion detection using sub-image based features through human facial expressions. In 2012 International Conference on Computer & Information Science (ICCIS) (Vol. 1, pp. 332-335). IEEE. doi: 10.1109/ICCISci.2012.6297264.

Ekman, P. and Friesen, W.V., 1971. Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2), p.124. doi: 10.1037/h0030377.

De, A. and Saha, A., 2015, March. A comparative study on different approaches of real time human emotion recognition based on facial expression detection. In 2015 International Conference on Advances in Computer Engineering and Applications (pp. 483-487). IEEE. doi: 10.1109/ICACEA.2015.7164792.

Özdil, A. and Özbilen, M.M., 2014, October. A survey on comparison of face recognition algorithms. In 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1-3). IEEE. doi: 10.1109/ICAICT.2014.7035956.

Sullivan, M.W. and Lewis, M., 2003. Emotional expressions of young infants and children: A practitioner's primer. Infants & Young Children, 16(2), pp.120-142. doi: 10.1097/00001163-200304000-00005.

Dang, K. and Sharma, S., 2017, January. Review and comparison of face detection algorithms. In 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence (pp. 629-633). IEEE. doi: 10.1109/CONFLUENCE.2017.7943228.

Viola, P. and Jones, M., 2001, December. 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 (Vol. 1, pp. I-I). Ieee. doi: 10.1109/CVPR.2001.990517.

Arya, S., Pratap, N. and Bhatia, K., 2015. Future of face recognition: a review. Procedia Computer Science, 58, pp.578-585. doi: 10.1016/j.procs.2015.08.076.

Rasyid, M.F., Zainuddin, Z. and Andani, A., 2019. Early Detection of Health Kindergarten Student at School Using Image Processing Technology. Accessed: Dec. 21, 2021. [Online]. Available:

Abdullah, M., Wazzan, M. and Bo-Saeed, S., 2012. Optimizing face recognition using PCA. arXiv preprint arXiv:1206.1515. doi: 10.5121/ijaia.2012.3203.

Turk, M., 1991. Pentland. Eigenfaces for recognition. K. Cogn. Neurosci, 4, pp.72-86. doi: 10.1162/jocn.1991.3.1.71.

Zhongli, M., Qianqian, L., Huixin, L. and Zuoyong, L., 2017, August. Image representation based PCA feature for image classification. In 2017 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 1121-1125). IEEE. doi: 10.1109/ICMA.2017.8015974.

Murinto, M., 2007. Pengenalan Wajah Manusia Dengan Metode Principle Component Analysis (PCA). TELKOMNIKA (Telecommunication Computing Electronics and Control), 5(3), pp.177-184. doi: 10.12928/telkomnika.v5i3.1364.

Belhumeur, P.N., Hespanha, J.P. and Kriegman, D.J., 1997. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), pp.711-720.

Hegde, N., Preetha, S. and Bhagwat, S., 2018, September. Facial Expression Classifier Using Better Technique: FisherFace Algorithm. In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 604-610). IEEE. doi: 10.1109/ICACCI.2018.8554499.

Yohanes, B.W., Airlangga, R.D. and Setyawan, I., 2018, August. Real Time Face Recognition Comparison Using Fisherfaces and Local Binary Pattern. In 2018 4th International Conference on Science and Technology (ICST) (pp. 1-5). IEEE. doi: 10.1109/ICSTC.2018.8528608.

Ahonen, T., Hadid, A. and Pietikainen, M., 2006. Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), pp.2037-2041. doi: 10.1109/TPAMI.2006.244.

Ahonen, T., Hadid, A. and Pietikäinen, M., 2004, May. Face recognition with local binary patterns. In European conference on computer vision (pp. 469-481). Springer, Berlin, Heidelberg. doi: 10.1007/978-3-540-24670-1_36.

Pietikäinen, M., 2010. Local Binary Patterns, Scholarpedia, No. 3, Vol. 5, 9775. doi: 10.4249/scholarpedia.9775.