Abstract
We explore the performance of the established state-of-the-art Convolutional Neural Network (CNN) models on the novel face recognition data set with artistic facial makeup and occlusions. The strength and weaknesses of different CNN architectures are probed on particular types of makeup and occlusions of the benchmark data. Apart from the practical value of the knowing effectiveness of the face camouflaging techniques, such a data set magnifies the reliability and robustness problem of the established CNN models in real-life settings. A flexible and lightweight approach of isolating uncertainty of the CNN models verdicts’ trustworthiness is investigated, aiming to increase the trusted recognition accuracy. A separate supervising Artificial Neural Network (ANN) is attached to the established CNNs and is trained to learn patterns of the erroneous classifications of the underlying CNN models.
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
Set, Face Matching Data., \(\vert \) Biometric Data \(\vert \) CyberExtruder, December 2019
Agarap, A.F.: Deep learning using rectified linear units (relu) (2018)
Akter, M., Jakaite, L.: Extraction of texture features from x-ray images: case of osteoarthritis detection. In: Yang, X.S., Sherratt, S., Dey, N., Joshi, A. (eds.) Third International Congress on Information and Communication Technology, pp. 143–150. Springer (2019)
Amodei, D., Olah, C., Steinhardt, J., Schulman, J., Mané, D.: Concrete problems in AI safety, Paul Christiano (2016)
Andrychowicz, M.: Learning to learn by gradient descent by gradient descent (2016)
Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q., (eds.), Advances in Neural Information Processing Systems, volume 24. Curran Associates Inc. (2011)
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, pp. 1–8, Feburary 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, November 2011
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, September 2013
Feng ,R., Prabhakaran, B.: Facilitating fashion camouflage art. In: Proceedings of the 21st ACM International Conference on Multimedia, MM 2013, pages 793–802, New York, NY, USA. ACM (2013)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E., Ferencz, A., Jurie, F.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: ’Real-Life’ Images: Detection, Alignment, and Recognition, Marseille, France (2008)
Izenman, A.J.: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer Publishing Company, Incorporated (2008)
Jakaite, L., Schetinin, V., Hladuvka, J., Minaev, S., Ambia, A., Krzanowski, W.: Deep learning for early detection of pathological changes in x-ray bone microstructures: case of osteoarthritis. Sci. Rep. 11 (2021)
Jakaite, L., Schetinin, V., Maple, C.: Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms. Comput. Math. Meth. Med. 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 (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 (2011)
Jia, S., Li, X., Hu, C., Guo, G., Xu, Z.: 3D face anti-spoofing with factorized bilinear coding (2020)
Khodabakhsh, A., Busch, C.: A generalizable deepfake detector based on neural conditional distribution modelling. In: 2020 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–5 (2020)
Kurd, Z., Kelly, T.: Establishing safety criteria for artificial neural networks. In: Palade, V., Howlett, R.J., Jain, L. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, vol. 2773. Springer, Berlin, Heidelberg (2003)
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 (2018)
Lake, B.M., Ullman, T.D., Tenenbaum, J.D., Gershman, S.J.: Building machines that learn and think like people (2016)
Lecun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: The Handbook of Brain Theory and Neural Networks, MIT Press (1995)
Liu, X., Wang, X., Matwin, S.: Interpretable deep convolutional neural networks via meta-learning. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–9 (2018)
Mansourifar, H., Shi, W.: One-shot gan generated fake face detection (2020)
Martinez, A., Benavente, R.: The AR face database. Technical Report 24, Computer Vision Center, Bellatera, June 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, 21–25 March 2011, Santa Barbara, CA, USA (2011)
Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms (2018)
Nyah, N., Jakaite, L., Schetinin, V., Sant, P., Aggoun, A.: Evolving polynomial neural networks for detecting abnormal patterns. In: 2016 IEEE 8th International Conference on Intelligent Systems, pp. 74–80 (2016)
Nyah, N., Jakaite, L., Schetinin, V., Sant, P., Aggoun, A.: Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data. In: 2016 SAI Computing Conference, pp. 409–413 (2016)
Ram, R., Müller, S., Pfreundt, F.-J., Gauger, N.R., Keuper, J.: Scalable hyperparameter optimization with lazy Gaussian processes (2020)
Santoro, A.B., Sergey, B., Matthew, W., Daan, L.T.: Meta-learning with memory-augmented neural networks. In: Balcan, M.F., Weinberger, K.Q., (eds.), Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, PMLRpp. 1842–1850, New York, New York, USA, 20–22 June 2016
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)
Schetinin, V., Jakaite, L., Krzanowski, W.: Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance. Integr. Comput.-Aid. Eng. 26, 1–17 (2018)
Schetinin, V., Jakaite, L., Schult, J.: Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation. In: 24th International Symposium on Computer-Based Medical Systems, pp. 1–6 (2011)
Selitskaya, N., Sielicki, S., Christou, N.: Challenges in face recognition using machine learning algorithms: case of makeup and occlusions. In: Arai, K., Kapoor, S., Bhatia, R., (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing. Springer (2020)
Christou, N., Selitskaya, N., Sielicki, S.: Challenges in real-life face recognition with heavy makeup and occlusions using deep learning algorithms. In: Nicosia, G., et al., (Eds.) Machine Learning, Optimization, and Data Science 6th International Conference, LOD 2020, Siena, Italy, 19–23 July 2020, Revised Selected Papers, Part II, volume 12566. Springer International Publishing (2020)
Selitskiy, S.: Code for paper ‘Learning Incorrect Verdict Patterns of the Established Face Recognising CNN Models using Meta-learning Supervisor ANN’, January 2021
Selitskiy, S., Selitskaya, N., Marina, K.: BookClub artistic makeup and occlusions face data. Mendeley Data, 2 September 2020
Pratt, L., Thrun, S.: Learning To Learn. Springer, Boston, MA (1998)
Vanschoren, J.: Meta-learning: A survey (2018)
Willers, O., Sudholt, S., Raafatnia, S., Abrecht, S.: Safety concerns and mitigation approaches regarding the use of deep learning in safety-critical perception tasks (2020)
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning to recognize patch-wise consistency for deepfake detection (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Selitskiy, S., Christou, N., Selitskaya, N. (2022). Learning Incorrect Verdict Patterns of the Established Face Recognizing CNN Models Using Meta-Learning Supervisor ANN. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-82196-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82195-1
Online ISBN: 978-3-030-82196-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)