Abstract
This paper presents an automatic facial analysis system which is able to perform gender detection, hair segmentation and geometry detection, color attributes extraction (hair, skin, eyebrows, eyes and lips), accessories (eyeglasses) analysis from facial images. For the more complex tasks (gender detection, hair segmentation, eyeglasses detection) we used state of the art convolutional neural networks, and for the other tasks we used classical image processing algorithms based on geometry and appearance models. When data was available, the proposed system was evaluated on public datasets. An acceptance study was also performed to assess the performance on the system in real life scenarios.
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
References
Borza, D., Itu, R., Danescu, R.: In the eye of the deceiver: analyzing eye movements as a cue to deception. J. Imaging 4(10), 120 (2018)
Dantcheva, A., Erdogmus, N., Dugelay, J.L.: On the reliability of eye color classification as a soft biometrics trait. In: Proceedings of WACV (2011, to appear)
Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9(12), 2170–2179 (2014)
Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)
Harville, M., Baker, H., Bhatti, N., Susstrunk, S.: Consistent image-based measurement and classification of skin color. In: IEEE International Conference on Image Processing 2005, vol. 2, pp. II–374. IEEE (2005)
Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.: The importance of small pupils: a study of how pupil dilation affects iris biometrics. In: 2008 IEEE Conference on Biometrics: Theory, Applications and Systems. pp. 1–6. IEEE (2008)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Jmal, M., Mseddi, W.S., Attia, R., Youssef, A.: Classification of human skin color and its application to face recognition. In: The Sixth International Conferences on Advances in Multimedia, MMEDIA. Citeseer (2014)
Kae, A., Sohn, K., Lee, H., Learned-Miller, E.: Augmenting CRFs with Boltzmann machine shape priors for image labeling (2013)
Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867–1874 (2014)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Liu, Z., Luo, P., Wang, X., Tang, X.: Large-scale CelebFaces attributes (CelebA) dataset (2018). Accessed 15 Aug 2018
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Ma, D.S., Correll, J., Wittenbrink, B.: The Chicago face database: a free stimulus set of faces and norming data. Behav. Res. Methods 47(4), 1122–1135 (2015)
Muhammad, U.R., Svanera, M., Leonardi, R., Benini, S.: Hair detection, segmentation, and hairstyle classification in the wild. Image Vis. Comput. 71, 25–37 (2018)
Rousset, C., Coulon, P.Y.: Frequential and color analysis for hair mask segmentation. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 2276–2279. IEEE (2008)
Shen, Y., Peng, Z., Zhang, Y.: Image based hair segmentation algorithm for the application of automatic facial caricature synthesis. Sci. World J. 2014 (2014)
Yacoob, Y., Davis, L.: Detection, analysis and matching of hair. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 741–748. IEEE (2005)
Yoon, S., Harville, M., Baker, H., Bhatii, N.: Automatic skin pixel selection and skin color classification. In: 2006 International Conference on Image Processing, pp. 941–944. IEEE (2006)
Acknowledgements
This work was made possible with the support of the “Institute of Research, Development and Education in Computer Vision” (Asociația Institutul de Cercetare, Dezvoltare și Educație în Viziune Artificială, http://icvcluj.eu/), Cluj-Napoca, Romania, and Tapptitude (https://tapptitude.com/), a product development agency, where the first iteration of the algorithm was built.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Borza, D., Itu, R., Danescu, R., Barbantan, I. (2020). Analysing Facial Features Using CNNs and Computer Vision. In: Simian, D., Stoica, L. (eds) Modelling and Development of Intelligent Systems. MDIS 2019. Communications in Computer and Information Science, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-030-39237-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-39237-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39236-9
Online ISBN: 978-3-030-39237-6
eBook Packages: Computer ScienceComputer Science (R0)