Abstract
We study a Face Recognition problem caused by unforeseen in the training set variations in face images, related to artistic makeup and other occlusions. Existing Artificial Neural Networks (ANNs) have achieved a high recognition accuracy; however, in the presence of significant variations, they perform poorly. We introduce a new data set of face images with variable makeup, hairstyles and occlusions, named BookClub artistic makeup face data, and then examine the performance of the ANNs under different conditions. In our experiments, the recognition accuracy has decreased when the test images include an unseen type of the makeup and occlusions, happened in a real-world scenario. We show that the fusion off the training set with several heavy makeup and other occlusion images can improve the performance.
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
Face Matching Data Set \(\vert \) Biometric Data \(\vert \) CyberExtruder (Dec 2019). https://cyberextruder.com/face-matching-data-set-download. Accessed 8 Dec 2019
Transfer Learning Using AlexNet Example (Dec 2019). https://www.mathworks.com/help/deeplearning/examples/transfer-learning-using-alexnet.html. Accessed 8 Dec 2019
Agarap, A.F.: Deep Learning Using Rectified Linear Units (RELU) (2018)
Chen, C., Dantcheva, A., Swearingen, T., Ross, A.: Spoofing faces using makeup: an investigative study. In: 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1–8 (Feb 2017)
Chen, C., Dantcheva, A., Ross, A.: Automatic facial makeup detection with application in face recognition. In: 2013 International Conference on Biometrics (ICB), pp. 1–8 (2013)
Colombo, A., Cusano, C., Schettini, R.: UMB-DB: a database of partially occluded 3d faces. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2113–2119 (Nov 2011)
Dantcheva, A., Chen, C., Ross, A.: Can facial cosmetics affect the matching accuracy of face recognition systems? In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 391–398 (Sep 2012)
Eckert, M., Kose, N., Dugelay, J.: Facial cosmetics database and impact analysis on automatic face recognition. In: 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), pp. 434–439 (Sep 2013)
Feng, R., Prabhakaran, B.: Facilitating fashion camouflage art. In: Proceedings of the 21st ACM International Conference on Multimedia MM 2013, pp. 793–802. ACM, New York, NY, USA (2013)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in ’Real-Life’ Images: Detection, Alignment, and Recognition. Erik Learned-Miller and Andras Ferencz and Frédéric Jurie, Marseille, France (Oct 2008)
Izenman, A.J.: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer Publishing Company Incorporated, New York (2008)
Jakaite, L., Schetinin, V., Maple, C.: Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms. Comput. Math. Methods Med. pp. 1–7 (2012)
Jakaite, L., Schetinin, V., Maple, C., Schult, J.: Bayesian decision trees for EEG assessment of newborn brain maturity. In: The 10th Annual Workshop on Computational Intelligence UKCI 2010 (2010)
Jakaite, L., Schetinin, V., Schult, J.: Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity. In: 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6. Bristol (June 2011)
Kushwaha, V., Singh, M., Singh, R., Vatsa, M., Ratha, N., Chellappa, R.: Disguised faces in the wild. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–18 (June 2018)
Lecun, Y., Bengio, Y.: Convolutional Networks For Images, Speech. And Time-series. MIT Press, Cambridge (1995)
Martinez, A., Benavente, R.: The ar face database. Technical report. 24, Computer Vision Center, Bellatera (Jun 1998)
Min, R., Hadid, A., Dugelay, J.L.: Improving the recognition of faces occluded by facial accessories. In: FG 2011, 9th IEEE Conference on Automatic Face and Gesture Recognition, March 21–25, 2011, Santa Barbara, CA, USA (03 2011)
N. Selitskaya, S. Sielitsky, M.K.: Bookclub data set (Jun 2019). https://drive.google.com/file/d/18nH8m_ST-kqcAWZp_vjdo-nS3cESCBOO/view. Accessed 15 June 2019
Schetinin, V., Jakaite, L., Krzanowski, W.: Bayesian averaging over decision tree models: an application for estimating uncertainty in trauma severity scoring. Int. J. Med. Inform 112, 6–14 (2018)
Schetinin, V., Jakaite, L., Krzanowski, W.: Bayesian averaging over decision tree models for trauma severity scoring. Artif. Intell. Med. 84, 139–145 (2018). (2.0), (2.0)
Setty, S., Husain, M., Beham, P., Gudavalli, J., Kandasamy, M., Vaddi, R., Hemadri, V., Karure, J.C., Raju, R., Rajan, B., Kumar, V., Jawahar, C.V.: Indian movie face database: A benchmark for face recognition under wide variations. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). pp. 1–5 (Dec 2013)
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
Selitskaya, N., Sielicki, S., Christou, N. (2020). Challenges in Real-Life Face Recognition with Heavy Makeup and Occlusions Using Deep Learning Algorithms. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-64580-9_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64579-3
Online ISBN: 978-3-030-64580-9
eBook Packages: Computer ScienceComputer Science (R0)