{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T16:41:16Z","timestamp":1776012076531,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T00:00:00Z","timestamp":1489968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.<\/jats:p>","DOI":"10.3390\/s17030637","type":"journal-article","created":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T11:39:09Z","timestamp":1490009949000},"page":"637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction"],"prefix":"10.3390","volume":"17","author":[{"given":"Dat","family":"Nguyen","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Ki","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Hyung","family":"Hong","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Ja","family":"Koo","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Min","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]},{"given":"Kang","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/34.868683","article-title":"Real-time surveillance of people and their activities","volume":"22","author":"Haritaoglu","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.procs.2016.03.052","article-title":"Automatic traffic surveillance using video tracking","volume":"79","author":"Namade","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.cviu.2015.11.014","article-title":"Temporal mapping of surveillance video for indexing and summarization","volume":"144","author":"Bagheri","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/978-3-642-32695-0_31","article-title":"Recognizing human gender in computer-vision: A survey","volume":"7458","author":"Ng","year":"2012","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., and Park, K.R. (2016). Body-based gender recognition using images from visible and thermal cameras. Sensors, 16.","DOI":"10.3390\/s16020156"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1016\/j.patrec.2008.03.016","article-title":"An experimental comparison of gender classification methods","volume":"29","author":"Makinen","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1016\/j.eswa.2014.11.023","article-title":"Boosting gender recognition performance with a fuzzy inference system","volume":"42","author":"Danisman","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.patrec.2013.04.028","article-title":"Robust gender recognition by exploiting facial attributes dependencies","volume":"36","author":"Buenaposada","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Cao, L., Dikmen, M., Fu, Y., and Huang, T.S. (2008, January 27\u201331). Gender recognition from body. Proceedings of the 16th ACM International Conference on Multimedia, Vancouver, BC, Canada.","DOI":"10.1145\/1459359.1459470"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1007\/978-3-642-12297-2_23","article-title":"Gender from body: A biologically-inspired approach with manifold learning","volume":"5996","author":"Guo","year":"2009","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., and Park, K.R. (2016). Enhanced gender recognition system using an improved histogram of oriented gradient (HOG) feature from quality assessment of visible light and thermal images of the human body. Sensors, 16.","DOI":"10.3390\/s16071134"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TIFS.2013.2291969","article-title":"Human identity and gender recognition from gait sequences with arbitrary walking directions","volume":"9","author":"Lu","year":"2014","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/TIP.2009.2020535","article-title":"A study on gait-based gender classification","volume":"18","author":"Yu","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TSMCC.2011.2104950","article-title":"Gender recognition using 3-D human body shapes","volume":"41","author":"Tang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tang, J., Liu, X., Cheng, H., and Robinette, K.M. (2012, January 14\u201317). Gender recognition with limited feature points from 3-D human body shapes. Proceedings of the IEEE International Conference on System, Man and Cybernetics, Seoul, Korea.","DOI":"10.1109\/ICSMC.2012.6378116"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10580","DOI":"10.3390\/s150510580","article-title":"Robust pedestrian detection by combining visible and thermal infrared cameras","volume":"15","author":"Lee","year":"2015","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21898","DOI":"10.3390\/s150921898","article-title":"Human age estimation method robust to camera sensor and\/or face movement","volume":"15","author":"Nguyen","year":"2015","journal-title":"Sensors"},{"key":"ref_19","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histogram of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_21","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M.A., and Wolf, L. (2014, January 23\u201328). DeepFace: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, F., Shen, C., and Lin, G. (2015, January 7\u201312). Deep convolutional neural fields for depth estimation from a single image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299152"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahmed, E., Jones, M., and Marks, T.K. (2015, January 7\u201312). An improved deep learning architecture for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299016"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., and Zheng, N. (2016, January 27\u201330). Person re-identification by multi-channel parts-based CNN with improved triplet loss function. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, S., Xing, J., Niu, Z., Shan, S., and Yan, S. (2015, January 7\u201312). Shape driven kernel adaptation in convolutional neural network for robust facial trait recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298618"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sugano, Y., Fritz, M., and Bulling, A. (2015, January 7\u201312). Appearance-based gaze estimation in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299081"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gurghian, A., Koduri, T., Bailur, S.V., Carey, K.J., and Murali, V.N. (2016, January 27\u201330). DeepLanes: End-to-end lane position estimation using deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.12"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W., and Torralba, A. (2016, January 27\u201330). Eye tracking for everyone. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.239"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Qin, H., Yan, J., Li, X., and Hu, X. (2016, January 27\u201330). Joint training of cascaded CNN for face detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.376"},{"key":"ref_31","unstructured":"Matlab Toolbox for Convolutional Neural Network. Available online: Http:\/\/www.mathworks.com\/help\/nnet\/convolutional-neural-networks.html."},{"key":"ref_32","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","unstructured":"LibSVM\u2014A Library for Support Vector Machines. Available online: https:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7157","DOI":"10.1016\/j.eswa.2008.08.052","article-title":"Selection and fusion of facial features for face recognition","volume":"36","author":"Fan","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6404","DOI":"10.1016\/j.eswa.2010.02.079","article-title":"Evaluation of face recognition techniques using PCA, wavelets and SVM","volume":"37","author":"Gumus","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_36","unstructured":"Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio, T. (1997, January 17\u201319). Pedestrian detection using wavelet templates. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico."},{"key":"ref_37","unstructured":"The MIT Dataset. Available online: http:\/\/cbcl.mit.edu\/software-datasets\/PedestrianData.html."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sch\u00fcldt, C., Laptev, I., and Caputo, B. (2004, January 26\u201326). Recognizing human actions: A local SVM approach. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1109\/TPAMI.2007.70711","article-title":"Actions as space-time shapes","volume":"29","author":"Gorelick","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s11263-008-0142-8","article-title":"Searching for complex human activities with no visual examples","volume":"80","author":"Ikizler","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","unstructured":"The LTIR Dataset v1.0. Available online: http:\/\/www.cvl.isy.liu.se\/en\/research\/datasets\/ltir\/version1.0\/."},{"key":"ref_42","unstructured":"OTCBVS Benchmark Dataset Collection (Dataset 03: OSU Color-Thermal Database). Available online: http:\/\/vcipl-okstate.org\/pbvs\/bench\/."},{"key":"ref_43","unstructured":"Dongguk Body-based Gender Recognition Database (DBGender-DB1). Available online: http:\/\/dm.dongguk.edu\/link.html\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.3390\/s140203095","article-title":"Finger-vein image enhancement using a fuzzy-based fusion method with Gabor and Retinex filtering","volume":"14","author":"Shin","year":"2014","journal-title":"Sensors"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/TCE.2012.6227456","article-title":"An embedded real-time finger-vein recognition system for mobile devices","volume":"58","author":"Liu","year":"2012","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.patrec.2011.08.016","article-title":"New iris recognition method for noisy iris images","volume":"33","author":"Shin","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.patrec.2015.07.008","article-title":"Iris recognition using multi-scale morphologic features","volume":"65","author":"Umer","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","first-page":"25","article-title":"Robustness of face recognition to variations of illumination on mobile devices based on SVM","volume":"4","author":"Nam","year":"2010","journal-title":"KSII Trans. Internet Inf. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/3\/637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:30:49Z","timestamp":1760207449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/3\/637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,20]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["s17030637"],"URL":"https:\/\/doi.org\/10.3390\/s17030637","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,20]]}}}