FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers
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[1] Arun Ross,et al. What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics , 2016, IEEE Transactions on Information Forensics and Security.
[2] Andrew Zisserman,et al. Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings , 2018, ECCV Workshops.
[3] Arun Ross,et al. Label propagation approach for predicting missing biographic labels in face-based biometric records , 2018, IET Biom..
[4] Brian C. Lovell,et al. Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks , 2018, ECCV.
[5] Shefali Arora,et al. A Robust Approach for Gender Recognition Using Deep Learning , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
[6] Damon L. Woodard,et al. Soft biometric classification using periocular region features , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).
[7] Richa Singh,et al. Data Fine-tuning , 2018, AAAI.
[8] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Roberto Paredes,et al. Local Deep Neural Networks for gender recognition , 2016, Pattern Recognit. Lett..
[10] Li Zhang,et al. Controllable Face Privacy , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[11] K. Bowyer,et al. Predicting ethnicity and gender from iris texture , 2011, 2011 IEEE International Conference on Technologies for Homeland Security (HST).
[12] Xin Zheng,et al. A Survey of Deep Facial Attribute Analysis , 2018, International Journal of Computer Vision.
[13] Vishnu Naresh Boddeti,et al. Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Blaz Meden,et al. k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification † , 2018, Entropy.
[15] Xiang Li,et al. A Method for Face Fusion Based on Variational Auto-Encoder , 2018, 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP).
[16] Erik Blasch,et al. GARP-face: Balancing privacy protection and utility preservation in face de-identification , 2014, IEEE International Joint Conference on Biometrics.
[17] Skyler T. Hawk,et al. Presentation and validation of the Radboud Faces Database , 2010 .
[18] G. Loewenstein,et al. What Is Privacy Worth? , 2013, The Journal of Legal Studies.
[19] Xiaoming Liu,et al. Disentangled Representation Learning GAN for Pose-Invariant Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Mahadev Satyanarayanan,et al. OpenFace: A general-purpose face recognition library with mobile applications , 2016 .
[21] Richa Singh,et al. Anonymizing k-Facial Attributes via Adversarial Perturbations , 2018, IJCAI.
[22] Els J. Kindt,et al. Privacy and Data Protection Issues of Biometric Applications , 2013 .
[23] Yong Xiang,et al. Protection of Privacy in Biometric Data , 2016, IEEE Access.
[24] Denton Bobeldyk,et al. Analyzing Covariate Influence on Gender and Race Prediction From Near-Infrared Ocular Images , 2018, IEEE Access.
[25] Denton Bobeldyk,et al. Predicting Soft Biometric Attributes from 30 Pixels: A Case Study in NIR Ocular Images , 2019, 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW).
[26] Terrance E. Boult,et al. AFFACT: Alignment-free facial attribute classification technique , 2016, 2017 IEEE International Joint Conference on Biometrics (IJCB).
[27] Patrick J. Flynn,et al. The prediction of old and young subjects from iris texture , 2013, 2013 International Conference on Biometrics (ICB).
[28] Thomas B. Moeslund,et al. On soft biometrics , 2015, Pattern Recognit. Lett..
[29] Arun Ross,et al. Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images , 2017, 2018 International Conference on Biometrics (ICB).
[30] Julian Fiérrez,et al. SensitiveNets: Learning Agnostic Representations with Application to Face Recognition , 2019, ArXiv.
[31] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[32] Peter Peer,et al. Selective Face Deidentification with End-to-End Perceptual Loss Learning , 2018, 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI).
[33] Sharath Pankanti,et al. Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.
[34] Arun Ross,et al. Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers , 2018, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).
[35] Arun Ross,et al. An introduction to biometrics , 2008, ICPR 2008.
[36] Nello Cristianini,et al. Right for the Right Reason: Training Agnostic Networks , 2018, IDA.
[37] Patrick J. Flynn,et al. Face Recognition Using 2D and 3D Facial Data , 2003 .
[38] Nalini K. Ratha,et al. Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..
[39] Ahmed M. Badawi,et al. Fingerprint-Based Gender Classification , 2006, IPCV.
[40] Tieniu Tan,et al. Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[41] Aythami Morales,et al. SensitiveNets: Learning Agnostic Representations with Application to Face Images. , 2020, IEEE transactions on pattern analysis and machine intelligence.
[42] Arun Ross,et al. Soft biometric privacy: Retaining biometric utility of face images while perturbing gender , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).
[43] Timothy F. Cootes,et al. Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..
[44] Naser Damer,et al. Unsupervised privacy-enhancement of face representations using similarity-sensitive noise transformations , 2019, Applied Intelligence.
[45] Fan Yang,et al. Privacy-Protective-GAN for Privacy Preserving Face De-Identification , 2019, Journal of Computer Science and Technology.
[46] Terrance E. Boult,et al. Are facial attributes adversarially robust? , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[47] Denton Bobeldyk,et al. Predicting Gender and Race from Near Infrared Iris and Periocular Images , 2018, ArXiv.
[48] Alessandro Acquisti,et al. Predicting Social Security numbers from public data , 2009, Proceedings of the National Academy of Sciences.
[49] Arun Ross,et al. Privacy of Facial Soft Biometrics: Suppressing Gender But Retaining Identity , 2014, ECCV Workshops.
[50] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[51] Karl Ricanek,et al. MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).
[52] Alberto Botana López,et al. Deep Learning in Biometrics: A Survey , 2019, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal.
[53] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[54] Stephen Milborrow. The MUCT Landmarked Face Database , 2010 .
[55] Nello Cristianini,et al. Gender Classification by Deep Learning on Millions of Weakly Labelled Images , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).