{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:18:02Z","timestamp":1781176682629,"version":"3.54.1"},"reference-count":88,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,13]],"date-time":"2018-01-13T00:00:00Z","timestamp":1515801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["P2-0250 (B)"],"award-info":[{"award-number":["P2-0250 (B)"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["P2-0214 (A)"],"award-info":[{"award-number":["P2-0214 (A)"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007065","name":"Nvidia","doi-asserted-by":"publisher","award":["Donation: TitanX GPU"],"award-info":[{"award-number":["Donation: TitanX GPU"]}],"id":[{"id":"10.13039\/100007065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.<\/jats:p>","DOI":"10.3390\/e20010060","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T12:30:36Z","timestamp":1516019436000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-4798","authenticated-orcid":false,"given":"Bla\u017e","family":"Meden","sequence":"first","affiliation":[{"name":"Faculty of Computer and Information Science, University of Ljubljana, Ve\u010dna pot 113, SI-1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u017diga","family":"Emer\u0161i\u010d","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Science, University of Ljubljana, Ve\u010dna pot 113, SI-1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3385-5780","authenticated-orcid":false,"given":"Vitomir","family":"\u0160truc","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka cesta 25, SI-1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9744-4035","authenticated-orcid":false,"given":"Peter","family":"Peer","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Science, University of Ljubljana, Ve\u010dna pot 113, SI-1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TKDE.2005.32","article-title":"Preserving privacy by de-identifying face images","volume":"17","author":"Newton","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","unstructured":"Gross, R., Airoldi, E., Malin, B., and Sweeney, L. (2006, January 28\u201330). Integrating Utility into Face De-identification. Proceedings of the 5th International Conference on Privacy Enhancing Technologies (PET\u201905), Cambridge, UK."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1049\/iet-spr.2017.0049","article-title":"Face deidentification with generative deep neural networks","volume":"11","author":"Meden","year":"2017","journal-title":"IET Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1142\/S0218488502001648","article-title":"K-Anonymity: A Model for Protecting Privacy","volume":"10","author":"Sweeney","year":"2002","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1217299.1217302","article-title":"L-diversity: Privacy Beyond K-Anonymity","volume":"1","author":"Machanavajjhala","year":"2007","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., and Venkatasubramanian, S. (2007, January 15\u201320). t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. Proceedings of the IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Meden, B., Emersic, Z., Struc, V., and Peer, P. (2017, January 10\u201312). \u03ba-Same-Net: Neural-Network-Based Face Deidentification. Proceedings of the IEEE International Conference and Workshop on Bioinspired Intelligence (IWOBI), Funchal, Portugal.","DOI":"10.1109\/IWOBI.2017.7985521"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Agrawal, D., and Aggarwal, C.C. (2001, January 21\u201323). On the Design and Quantification of Privacy Preserving Data Mining Algorithms. Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS \u201901), Santa Barbara, CA.","DOI":"10.1145\/375551.375602"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., and Yu, P.S. (2008). A General Survey of Privacy-Preserving Data Mining Models and Algorithms. Privacy-Preserving Data Mining, Springer.","DOI":"10.1007\/978-0-387-70992-5"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kova\u010d, J., and Peer, P. (2014). Human skeleton model based dynamic features for walking speed invariant gait recognition. Math. Probl. Eng., 2014.","DOI":"10.1155\/2014\/484320"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TPAMI.2014.2377748","article-title":"Re-Identification in the Function Space of Feature Warps","volume":"37","author":"Martinel","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kova\u010d, J., \u0160truc, V., and Peer, P. (2017). Frame-based classification for cross-speed gait recognition. Multimedia Tools Appl., 1\u201323.","DOI":"10.1007\/s11042-017-5469-0"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.patcog.2017.08.029","article-title":"Deep adaptive feature embedding with local sample distributions for person re-identification","volume":"73","author":"Wu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_14","unstructured":"Miller, J., Campan, A., and Truta, T.M. (2008, January 26). Constrained k-Anonymity: Privacy with generalization boundaries. Proceedings of the Practical Privacy-Preserving Data Mining, Atlanta, GA, USA."},{"key":"ref_15","first-page":"65","article-title":"P-Sensitive K-Anonymity with Generalization Constraints","volume":"3","author":"Campan","year":"2010","journal-title":"Trans. Data Priv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hellani, H., Kilany, R., and Sokhn, M. (2015, January 8\u20139). Towards internal privacy and flexible K-Anonymity. Proceedings of the 2015 International Conference on Applied Research in Computer Science and Engineering (ICAR), Beirut, Lebanon.","DOI":"10.1109\/ARCSE.2015.7338148"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ngonga Ngomo, A.C., and K\u0159emen, P. (2016, January 21\u201323). Towards Flexible K-Anonymity. Proceedings of the 7th International Conference on Knowledge Engineering and Semantic Web (KESW), Prague, Czech Republic.","DOI":"10.1007\/978-3-319-45880-9"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dufaux, F., and Ebrahimi, T. (2010, January 19\u201323). A framework for the validation of privacy protection solutions in video surveillance. Proceedings of the IEEE International Conference on Multimedia and Expo, Suntec City, Singapore.","DOI":"10.1109\/ICME.2010.5583552"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bhattarai, B., Mignon, A., Jurie, F., and Furon, T. (2014, January 3\u20135). Puzzling face verification algorithms for privacy protection. Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Atlanta, GA, USA.","DOI":"10.1109\/WIFS.2014.7084305"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Letournel, G., Bugeau, A., Ta, V.T., and Domenger, J.P. (2015, January 27\u201330). Face de-identification with expressions preservation. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351631"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Alonso, V.E., Enr\u00edquez-Caldera, R.A., and Sucar, L.E. (2017). Foveation: An alternative method to simultaneously preserve privacy and information in face images. J. Electron. Imaging, 26.","DOI":"10.1117\/1.JEI.26.2.023015"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Garrido, P., Valgaerts, L., Rehmsen, O., Thormaehlen, T., Perez, P., and Theobalt, C. (2014, January 23\u201328). Automatic Face Reenactment. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.537"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Stamminger, M., Theobalt, C., and Nie\u00dfner, M. (2016, January 27\u201330). Face2Face: Real-Time Face Capture and Reenactment of RGB Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.262"},{"key":"ref_24","unstructured":"Mart\u00ednez-ponte, I., Desurmont, X., Meessen, J., and Fran\u00e7ois Delaigle, J. (2011, January 24\u201326). Robust human face hiding ensuring privacy. Proceedings of the International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Melbourne, Australia."},{"key":"ref_25","unstructured":"Zhang, W., Cheung, S.S., and Chen, M. (2005, January 14). Hiding privacy information in video surveillance system. Proceedings of the IEEE International Conference on Image Processing, Genova, Italy."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Korshunov, P., and Ebrahimi, T. (2013, January 1\u20133). Using warping for privacy protection in video surveillance. Proceedings of the 18th International Conference on Digital Signal Processing (DSP), Fira, Greece.","DOI":"10.1109\/ICDSP.2013.6622791"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s00530-011-0246-9","article-title":"Chaos-cryptography based privacy preservation technique for video surveillance","volume":"18","author":"Rahman","year":"2012","journal-title":"Multimedia Syst."},{"key":"ref_28","unstructured":"Zha, H., Taniguchi, R.i., and Maybank, S. (2009, January 23\u201327). Person De-identification in Videos. Proceedings of the 9th Asian Conference on Computer Vision (ACCV), Xi\u2019an, China. Revised Selected Papers, Part III."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Korshunov, P., Araimo, C., Simone, F.D., Velardo, C., Dugelay, J.L., and Ebrahimi, T. (2012, January 17\u201319). Subjective study of privacy filters in video surveillance. Proceedings of the IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), Banff, AB, Canada.","DOI":"10.1109\/MMSP.2012.6343472"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Samarzija, B., and Ribaric, S. (2014, January 26\u201330). An approach to the de-identification of faces in different poses. Proceedings of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.1109\/MIPRO.2014.6859758"},{"key":"ref_31","unstructured":"Cremers, D., Reid, I., Saito, H., and Yang, M.H. (2014, January 1\u20135). Photorealistic Face De-Identification by Aggregating Donors\u2019 Face Components. Proceedings of the 12th Asian Conference on Computer Vision (ACCV), Singapore, Singapore. Revised Selected Papers, Part III."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Farrugia, R.A. (2014, January 26\u201330). Reversible De-Identification for lossless image compression using Reversible Watermarking. Proceedings of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.1109\/MIPRO.2014.6859760"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1049\/iet-spr.2017.0048","article-title":"Face, hairstyle and clothing colour de-identification in video sequences","volume":"11","year":"2017","journal-title":"IET Signal Process."},{"key":"ref_34","unstructured":"Brkic, K., Sikiric, I., Hrkac, T., and Kalafatic, Z. (July, January 26). I Know that Person: Generative Full Body and Face De-Identification of People in Images. Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Las Vegas, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gross, R., Sweeney, L., de la Torre, F., and Baker, S. (2008, January 23\u201328). Semi-supervised learning of multi-factor models for face de-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587369"},{"key":"ref_36","unstructured":"Gross, R., Sweeney, L., de la Torre, F., and Baker, S. (2006, January 17\u201322). Model-Based Face De-Identification. Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop (CVPRW\u201906), New York, NY, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Meng, L., Sun, Z., Ariyaeeinia, A., and Bennett, K.L. (2014, January 26\u201330). Retaining expressions on de-identified faces. Proceedings of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.1109\/MIPRO.2014.6859759"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sun, Z., Meng, L., and Ariyaeeinia, A. (2015, January 4\u20138). Distinguishable de-identified faces. Proceedings of the 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.","DOI":"10.1109\/FG.2015.7285019"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Karpov, A., Potapova, R., and Mporas, I. (2017, January 12\u201316). Retaining Expression on De-identified Faces. Proceedings of the 19th International Conference of Speech and Computer (SPECOM), Hatfield, UK.","DOI":"10.1007\/978-3-319-66429-3"},{"key":"ref_40","unstructured":"Du, L., Yi, M., Blasch, E., and Ling, H. (October, January 29). GARP-face: Balancing privacy protection and utility preservation in face de-identification. Proceedings of the IEEE International Joint Conference on Biometrics, Clearwater, FL, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.image.2016.05.020","article-title":"De-identification for privacy protection in multimedia content: A survey","volume":"47","author":"Ribaric","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_42","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (arXiv, 2014). Generative Adversarial Networks, arXiv."},{"key":"ref_43","unstructured":"Mirza, M., and Osindero, S. (arXiv, 2014). Conditional Generative Adversarial Nets, arXiv."},{"key":"ref_44","unstructured":"Sricharan, K., Bala, R., Shreve, M., Ding, H., Saketh, K., and Sun, J. (ArXiv, 2017). Semi-supervised Conditional GANs, ArXiv."},{"key":"ref_45","unstructured":"Radford, A., Metz, L., and Chintala, S. (arXiv, 2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, arXiv."},{"key":"ref_46","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (arXiv, 2017). Wasserstein GAN, arXiv."},{"key":"ref_47","unstructured":"Nowozin, S., Cseke, B., and Tomioka, R. (arXiv, 2016). f-GAN: Training Generative Neural Samplers Using Variational Divergence Minimization, arXiv."},{"key":"ref_48","unstructured":"Tran, L., Yin, X., and Liu, X. (arXiv, 2017). Representation Learning by Rotating Your Faces, arXiv."},{"key":"ref_49","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (arXiv, 2017). Progressive Growing of GANs for Improved Quality, Stability, and Variation, arXiv."},{"key":"ref_50","first-page":"692","article-title":"Learning to Generate Chairs, Tables and Cars with Convolutional Networks","volume":"39","author":"Dosovitskiy","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","unstructured":"Kingma, D.P., and Welling, M. (arXiv, 2013). Auto-Encoding Variational Bayes, arXiv."},{"key":"ref_52","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (arXiv, 2016). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"De Decker, B., Dittmann, J., Kraetzer, C., and Vielhauer, C. (2013, January 25\u201326). Achieving Anonymity against Major Face Recognition Algorithms. Proceedings of the 14th IFIP TC 6\/TC 11 International Conference of Communications and Multimedia Security (CMS), Magdeburg, Germany.","DOI":"10.1007\/978-3-642-40779-6"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1080\/02699930903485076","article-title":"Presentation and validation of the Radboud Faces Database","volume":"24","author":"Langner","year":"2010","journal-title":"Cognit. Emot."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Messer, K., Kittler, J., Sadeghi, M., Marcel, S., Marcel, C., Bengio, S., Cardinaux, F., Sanderson, C., Czyz, J., and Vandendorpe, L. (2003, January 9\u201311). Face Verification Competition on the XM2VTS Database. Proceedings of the 4th International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA\u201903), Guildford, UK.","DOI":"10.1007\/3-540-44887-X_112"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., and Matthews, I. (2010, January 13\u201318). The extended cohn-kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref_57","unstructured":"Kanade, T., Cohn, J.F., and Tian, Y. (2000, January 28\u201330). Comprehensive database for facial expression analysis. Proceedings of the Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France."},{"key":"ref_58","unstructured":"Ekman, P., Friesen, W.V., and Hager, J.C. (2002). FACS investigator\u2019s guide. A Human Face, Architectural Nexus."},{"key":"ref_59","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912), Lake Tahoe, NV, USA."},{"key":"ref_60","unstructured":"Parkhi, O., Vedaldi, A., and Zisserman, A. (September, January 29). Deep Face Recognition. Proceedings of the British Machine Vision Association (BMVC), Dundee, UK."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Comput. Vis. Pattern Recognit., 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_62","unstructured":"Iandola, F., Han, S., Moskewicz, M., Ashraf, K., Dally, W., and Keutzer, K. (arXiv, 2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size, arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Hadid, A., and Pietik\u00e4inen, M. (2004, January 11\u201314). Face recognition with local binary patterns. Proceedings of the 8th European Conference on Computer Vision (ECCV), Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1016\/j.patcog.2011.12.021","article-title":"Face recognition using the POEM descriptor","volume":"45","author":"Vu","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Grm, K., \u0160truc, V., Artiges, A., Caron, M., and Ekenel, H.K. (2017). Strengths and weaknesses of deep learning models for face recognition against image degradations. IET Biom.","DOI":"10.1049\/iet-bmt.2017.0083"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2016.08.139","article-title":"Ear Recognition: More Than a Survey","volume":"255","author":"Emersic","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kazemi, V., and Sullivan, J. (2014, January 23\u201328). One Millisecond Face Alignment with an Ensemble of Regression Trees. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.241"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Facial Landmark Detection by Deep Multi-task Learning. Proceedings of the 13th European Conference on Computer Vision\u2014ECCV, Zurich, Switzerland. Part VI.","DOI":"10.1007\/978-3-319-10599-4"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ren, S., Cao, X., Wei, Y., and Sun, J. (2014, January 23\u201328). Face Alignment at 3000 FPS via Regressing Local Binary Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.218"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Peng, X., Zhang, S., Yang, Y., and Metaxas, D.N. (2015, January 7\u201313). PIEFA: Personalized Incremental and Ensemble Face Alignment. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.442"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Jourabloo, A., and Liu, X. (2016, January 27\u201330). Large-pose Face Alignment via CNN-based Dense 3D Model Fitting. Proceedings of the IEEE Computer Vision and Pattern Recogntion, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.454"},{"key":"ref_72","unstructured":"Zhu, X., Lei, Z., Liu, X., Shi, H., and Li, S.Z. (\u20131, January 26). Face Alignment Across Large Poses: A 3D Solution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Jung, H., Lee, S., Yim, J., Park, S., and Kim, J. (2015, January 7\u201313). Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition. Proceedings of the the IEEE International Conference on Computer Vision (ICCV), Los Alamitos, CA, USA.","DOI":"10.1109\/ICCV.2015.341"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Dapogny, A., Bailly, K., and Dubuisson, S. (2015, January 7\u201313). Pairwise Conditional Random Forests for Facial Expression Recognition. Proceedings of the The IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.431"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Mollahosseini, A., Hasani, B., Salvador, M.J., Abdollahi, H., Chan, D., and Mahoor, M.H. (2016, January 27\u201330). Facial Expression Recognition from World Wild Web. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW.2016.188"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Afshar, S., and Ali Salah, A. (2016, January 27\u201330). Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW.2016.189"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Peng, X., Xia, Z., Li, L., and Feng, X. (2016, January 27\u201330). Towards Facial Expression Recognition in the Wild: A New Database and Deep Recognition System. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW.2016.192"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Gaj\u0161ek, R., \u0160truc, V., Dobri\u0161ek, S., and Miheli\u010d, F. (2009, January 6\u201310). Emotion recognition using linear transformations in combination with video. Proceedings of the Tenth Annual Conference of the International Speech Communication Association, Brighton, UK.","DOI":"10.21437\/Interspeech.2009-476"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, Y., Mu, G., and Guo, G. (2015, January 7\u201313). A Study on Apparent Age Estimation. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.43"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ranjan, R., Zhou, S., Cheng Chen, J., Kumar, A., Alavi, A., Patel, V.M., and Chellappa, R. (2015, January 7\u201313). Unconstrained Age Estimation With Deep Convolutional Neural Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.54"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Huo, Z., Yang, X., Xing, C., Zhou, Y., Hou, P., Lv, J., and Geng, X. (2016, January 27\u201330). Deep Age Distribution Learning for Apparent Age Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW.2016.95"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., and Hua, G. (2016, January 27\u201330). Ordinal Regression With Multiple Output CNN for Age Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.532"},{"key":"ref_83","unstructured":"Konda, J., and Peer, P. (2016, January 19\u201321). Estimating people\u2019s age from face images with convolutional neural networks. Proceedings of the 25th International Electrotechnical and Computer Science Conference, Portoroz, Slovenia."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.patrec.2015.02.006","article-title":"Learning to classify gender from four million images","volume":"58","author":"Jia","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patrec.2015.11.015","article-title":"Local Deep Neural Networks for gender recognition","volume":"70","author":"Mansanet","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Levi, G., and Hassner, T. (2015, January 7\u201313). Age and Gender Classification Using Convolutional Neural Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"ref_87","unstructured":"Ranjan, R., Patel, V.M., and Chellappa, R. (arXiv, 2016). HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition, arXiv."},{"key":"ref_88","unstructured":"Burkert, P., Trier, F., Afzal, M.Z., Dengel, A., and Liwicki, M. (arXiv, 2015). DeXpression: Deep Convolutional Neural Network for Expression Recognition, arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/1\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:51:09Z","timestamp":1760194269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/1\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,13]]},"references-count":88,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["e20010060"],"URL":"https:\/\/doi.org\/10.3390\/e20010060","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,13]]}}}