Visual and textual explainability for a biometric verification system based on piecewise facial attribute analysis

A. F. Abate, L. Cimmino, F. Narducci, C. Pero, Biometric face recognition based on landmark dynamics, 2020 IEEE Intl Conf on dependable, autonomic and secure computing, Intl Conf on pervasive intelligence and computing, Intl Conf on cloud and big data computing, Intl Conf on cyber science and technology congress (DASC/PiCom/CBDCom/CyberSciTech), IEEE, 2020, pp. 601–605. Molnar, 2020, Interpretable machine learning–a brief history, state-of-the-art and challenges, 417 Zheng, 2020, A survey of deep facial attribute analysis, Int. J. Comput. Vis., 128, 2002, 10.1007/s11263-020-01308-z Kumar, 2009, Attribute and simile classifiers for face verification, 365 Berg, 2013, Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation, 955 Chung, 2012, Deep attribute networks, Vol. 3, 1 Liu, 2015, Deep learning face attributes in the wild, 3730 Wang, 2016, Walk and learn: facial attribute representation learning from egocentric video and contextual data, 2295 Zhong, 2016, Leveraging mid-level deep representations for predicting face attributes in the wild, 3239 Rozsa, 2016, Adversarial diversity and hard positive generation, 25 Abate, 2019, Clustering facial attributes: narrowing the path from soft to hard biometrics, IEEE Access, 8, 9037, 10.1109/ACCESS.2019.2962010 Sethi, 2019, Residual codean autoencoder for facial attribute analysis, Pattern Recogn. Lett., 119, 157, 10.1016/j.patrec.2018.03.010 Zhuang, 2018, Multi-label learning based deep transfer neural network for facial attribute classification, Pattern Recogn., 80, 225, 10.1016/j.patcog.2018.03.018 Duan, 2020, A novel multi-task tensor correlation neural network for facial attribute prediction, 12 (1), 1 Berrahal, 2021, Augmented binary multi-labeled cnn for practical facial attribute classification, Indones. J. Electr. Eng. Comput. Sci., 23, 973 Lipton, 2018, The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery, Queue, 16, 31, 10.1145/3236386.3241340 Molnar, 2020 Brito, 2021, A short survey on machine learning explainability: an application to periocular recognition, Electronics, 10, 1861, 10.3390/electronics10151861 Carvalho, 2019, Machine learning interpretability: a survey on methods and metrics, Electronics, 8, 832, 10.3390/electronics8080832 Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189 Apley, 2020, Visualizing the effects of predictor variables in black box supervised learning models, J. Royal Stat. Soc. Series B Stat. Methodol., 82, 1059, 10.1111/rssb.12377 Ribeiro, 2016, “Why should i trust you?” explaining the predictions of any classifier, 1135 Lundberg, 2017, A unified approach to interpreting model predictions, 30 Liu, 2015, Deep learning face attributes in the wild Abate, 2020, Clustering facial attributes: narrowing the path from soft to hard biometrics, IEEE Access, 8, 9037, 10.1109/ACCESS.2019.2962010 Rudd, 2016, Moon: A mixed objective optimization network for the recognition of facial attributes, 19 Lee, 2020, Maskgan: Towards diverse and interactive facial image manipulation King, 2009, Dlib-ml: a machine learning toolkit, J. Mach. Learn. Res., 10, 1755 McInnes, 2018, Umap: Uniform manifold approximation and projection for dimension reduction da Cruz Brito, 2021 Krizhevsky, 2017, Imagenet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386