Automatic Face Shape Classification Via Facial Landmark Measurements

  • A.-I. Marinescu Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Mihail Kogălniceanu 1, 400084, Cluj-Napoca, Romania


This paper tackles the sensitive subject of face shape identification via near neutral-pose 2D images of human subjects. The possibility of extending to 3D facial models is also proposed, and would alleviate the need for the neutral stance. Accurate face shape classification serves as a vital building block of any hairstyle and eye-wear recommender system. Our approach is based on extracting relevant facial landmark measurements and passing them through a naive Bayes classifier unit in order to yield the final decision. The literature on this subject is particularly scarce owing to the very subjective nature of human face shape classification. We wish to contribute a robust and automatic system that performs this task and highlight future development directions on this matter.


[1] Bansode, N., and Sinha, P. Face shape classification based on region similarity, correlation and fractal dimensions. IJCSI International Journal of Computer Science Issues 13, 1 (2016).
[2] Ileni, T. A., Borza, D. L., and Darabant, A. S. Fast in-the-wild hair segmentation and color classification. In Visigrapp (2019).
[3] Ion Marinescu, A., Alexandru Ileni, T., and Sergiu Darabant, A. A versatile 3d face reconstruction from multiple images for face shape classification. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (Sep. 2019), pp. 1–6.
[4] Jiang, Z., Wu, Q., Chen, K., and Zhang, J. Disentangled representation learning for 3d face shape. CoRR abs/1902.09887 (2019).
[5] Kazemi, V., and Sullivan, J. One millisecond face alignment with an ensemble of regression trees. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2014).
[6] Ma, D., Correll, J., and Wittenbrink, B. The chicago face database: A free stimulus set of faces and norming data. Behavior research methods 47 (01 2015).
[7] Marinescu, A. I., Dar˘ abant, A. S., and Ileni, T. A. ˘ A fast and robust, forehead-augmented 3d face reconstruction from multiple images using geometrical methods. In 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (Sep. 2020), pp. 1–6.
[8] Pasupa, K., Sunhem, W., and Chu Kiong, L. A hybrid approach to building face shape classifier for hairstyle recommender system. Expert Systems with Applications 120 (11 2018).
[9] Ronneberger, O., Fischer, P., and Brox, T. U-net: Convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015).
[10] Rosin, P. Measuring shape: Ellipticity, rectangularity, and triangularity. Machine Vision and Applications 14 (08 2001).
[11] Russell, S., and Norvig, P. Artificial Intelligence: A Modern Approach, 3 ed. Prentice Hall, 2010.
[12] Simonyan, K., and Zisserman, A. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015), Y. Bengio and Y. LeCun, Eds.
[13] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015).
[14] Tio, A. E. D. Face shape classification using inception v3. In Electrical and Electronics Engineering Institute, University of the Philippines Diliman, Quezon City, Philippines (2017).
How to Cite
MARINESCU, A.-I.. Automatic Face Shape Classification Via Facial Landmark Measurements. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 66, n. 2, p. 69-78, dec. 2021. ISSN 2065-9601. Available at: <>. Date accessed: 27 jan. 2022. doi: