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In this article, we investigate the impact of deep learning in the field of biometrics, given its success in other domains. Since biometrics deals with identifying people by using their characteristics, it primarily involves supervised learning and can leverage the success of deep learning in other related domains. In this article, we survey 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.<\/jats:p>","DOI":"10.1145\/3190618","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T15:08:42Z","timestamp":1527088122000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":241,"title":["Deep Learning for Biometrics"],"prefix":"10.1145","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9668-4181","authenticated-orcid":false,"given":"Kalaivani","family":"Sundararajan","sequence":"first","affiliation":[{"name":"University of Florida, Gainesville, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damon L.","family":"Woodard","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,5,23]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2012. 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In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS\u201909) , Vol. 1 . 3. Ruslan Salakhutdinov and Geoffrey E Hinton. 2009. Deep Boltzmann machines. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS\u201909), Vol. 1. 3."},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738889"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS.2016.7791163"},{"key":"e_1_2_1_127_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICB.2016.7550060"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI.2015.16"},{"key":"e_1_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1109\/SLT.2016.7846260"},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2017.118"},{"key":"e_1_2_1_133_1","volume-title":"Proceedings of the Odyssey Conference. 109--116","author":"Stafylakis Themos","year":"2012","unstructured":"Themos Stafylakis , Patrick Kenny , Mohammed Senoussaoui , and Pierre Dumouchel . 2012 . Preliminary investigation of Boltzmann machine classifiers for speaker recognition . In Proceedings of the Odyssey Conference. 109--116 . Themos Stafylakis, Patrick Kenny, Mohammed Senoussaoui, and Pierre Dumouchel. 2012. Preliminary investigation of Boltzmann machine classifiers for speaker recognition. In Proceedings of the Odyssey Conference. 109--116."},{"key":"e_1_2_1_134_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952518"},{"key":"e_1_2_1_135_1","unstructured":"Yi Sun Yuheng Chen Xiaogang Wang and Xiaoou Tang. 2014a. Deep learning face representation by joint identification-verification. In Advances in Neural Information Processing Systems. 1988--1996.   Yi Sun Yuheng Chen Xiaogang Wang and Xiaoou Tang. 2014a. Deep learning face representation by joint identification-verification. In Advances in Neural Information Processing Systems. 1988--1996."},{"key":"e_1_2_1_136_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.244"},{"key":"e_1_2_1_137_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298907"},{"key":"e_1_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.525"},{"key":"e_1_2_1_140_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900298"},{"key":"e_1_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_1_142_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.220"},{"key":"e_1_2_1_143_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298891"},{"key":"e_1_2_1_144_1","unstructured":"Luan Tran Xi Yin and Xiaoming Liu. 2017. Representation learning by rotating your faces. arXiv:1705.11136.  Luan Tran Xi Yin and Xiaoming Liu. 2017. Representation learning by rotating your faces. arXiv:1705.11136."},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.07.028"},{"key":"e_1_2_1_146_1","volume-title":"Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR\u201907)","author":"Vargas J. Francisco","unstructured":"J. Francisco Vargas , Miguel A. Ferrer , Carlos M. Travieso , and Jes\u00fas B. Alonso . 2007. Off-line handwritten signature GPDS-960 corpus . In Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR\u201907) . J. Francisco Vargas, Miguel A. Ferrer, Carlos M. Travieso, and Jes\u00fas B. Alonso. 2007. Off-line handwritten signature GPDS-960 corpus. In Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR\u201907)."},{"key":"e_1_2_1_147_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6854363"},{"key":"e_1_2_1_148_1","unstructured":"Vasileios Vasilakakis Sandro Cumani and Pietro Laface. 2013. Speaker Recognition by Means of Deep Belief Networks. Retrieved from https:\/\/cls.ru.nl\/staff\/dvleeuwen\/btfs-2013\/vasilakakis-btfs2013.pdf.  Vasileios Vasilakakis Sandro Cumani and Pietro Laface. 2013. Speaker Recognition by Means of Deep Belief Networks. Retrieved from https:\/\/cls.ru.nl\/staff\/dvleeuwen\/btfs-2013\/vasilakakis-btfs2013.pdf."},{"key":"e_1_2_1_149_1","doi-asserted-by":"publisher","DOI":"10.5555\/1886063.1886084"},{"key":"e_1_2_1_150_1","volume-title":"Proceedings of the IEEE 23rd International Conference on Pattern Recognition (ICPR\u201916)","author":"Wang Ruxin","year":"2016","unstructured":"Ruxin Wang , Congying Han , and Tiande Guo . 2016 . A novel fingerprint classification method based on deep learning . In Proceedings of the IEEE 23rd International Conference on Pattern Recognition (ICPR\u201916) . IEEE, 931--936. Ruxin Wang, Congying Han, and Tiande Guo. 2016. A novel fingerprint classification method based on deep learning. In Proceedings of the IEEE 23rd International Conference on Pattern Recognition (ICPR\u201916). IEEE, 931--936."},{"key":"e_1_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2015.77"},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1577078"},{"key":"e_1_2_1_153_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"e_1_2_1_154_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995566"},{"key":"e_1_2_1_155_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7533144"},{"key":"e_1_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2545669"},{"key":"e_1_2_1_157_1","doi-asserted-by":"publisher","DOI":"10.3390\/s120404633"},{"key":"e_1_2_1_158_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2013-686"},{"key":"e_1_2_1_159_1","doi-asserted-by":"publisher","DOI":"10.1109\/CISP.2015.7407957"},{"key":"e_1_2_1_160_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISPACS.2016.7824728"},{"key":"e_1_2_1_161_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-25948-0_3"},{"key":"e_1_2_1_162_1","volume-title":"Proceedings of the Asian Conference on Computer Vision. 144--158","author":"Yi Dong","unstructured":"Dong Yi , Zhen Lei , and Stan Z. Li . 2014. Age estimation by multi-scale convolutional network . In Proceedings of the Asian Conference on Computer Vision. 144--158 . Dong Yi, Zhen Lei, and Stan Z. Li. 2014. Age estimation by multi-scale convolutional network. In Proceedings of the Asian Conference on Computer Vision. 144--158."},{"key":"e_1_2_1_163_1","unstructured":"Weidong Yin Yanwei Fu Leonid Sigal and Xiangyang Xue. 2017. Semi-Latent GAN: Learning to generate and modify facial images from attributes. arXiv:1704.02166.  Weidong Yin Yanwei Fu Leonid Sigal and Xiangyang Xue. 2017. Semi-Latent GAN: Learning to generate and modify facial images from attributes. arXiv:1704.02166."},{"key":"e_1_2_1_164_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2006.67"},{"key":"e_1_2_1_165_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"e_1_2_1_166_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472194"},{"key":"e_1_2_1_167_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1227981"},{"key":"e_1_2_1_168_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2016.97"},{"key":"e_1_2_1_169_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.212"},{"key":"e_1_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICB.2016.7550079"},{"key":"e_1_2_1_171_1","doi-asserted-by":"publisher","DOI":"10.1049\/cp.2015.0942"},{"key":"e_1_2_1_172_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2636093"},{"key":"e_1_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2012.6239225"},{"key":"e_1_2_1_174_1","unstructured":"Erjin Zhou Zhimin Cao and Qi Yin. 2015. Naive-deep face recognition: Touching the limit of LFW benchmark or not? arXiv:1501.04690.  Erjin Zhou Zhimin Cao and Qi Yin. 2015. Naive-deep face recognition: Touching the limit of LFW benchmark or not? arXiv:1501.04690."},{"key":"e_1_2_1_175_1","volume-title":"Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR\u201916)","author":"Zhu Linnan","year":"2016","unstructured":"Linnan Zhu , Keze Wang , Liang Lin , and Lei Zhang . 2016 . Learning a lightweight deep convolutional network for joint age and gender recognition . In Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR\u201916) . IEEE, 3282--3287. Linnan Zhu, Keze Wang, Liang Lin, and Lei Zhang. 2016. Learning a lightweight deep convolutional network for joint age and gender recognition. In Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR\u201916). 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