{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:46:21Z","timestamp":1732038381425},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T00:00:00Z","timestamp":1559779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes\u2014mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all six classes. We use the probability maps as gender descriptors and trained a Random Decision Forest (RDF) classifier, which classifies the face images as either male or female. The performance of the proposed framework is assessed on four publicly available datasets, namely Adience, LFW, FERET, and FEI, with results outperforming state-of-the-art (SOA).<\/jats:p>","DOI":"10.3390\/sym11060770","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T07:56:31Z","timestamp":1559894191000},"page":"770","source":"Crossref","is-referenced-by-count":38,"title":["Automatic Gender Classification through Face Segmentation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0864-5255","authenticated-orcid":false,"given":"Khalil","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzafarabbad 13100, Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7237-180X","authenticated-orcid":false,"given":"Muhammad","family":"Attique","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Sejong University, Seoul 05006, Korea"}]},{"given":"Ikram","family":"Syed","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Azad Jammu and Kashmir, Muzafarabbad 13100, Pakistan"}]},{"given":"Asma","family":"Gul","sequence":"additional","affiliation":[{"name":"Department of Statistics, Shaheed Benazir Bhutto Women University, Peshawar 25000, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,6]]},"reference":[{"key":"ref_1","unstructured":"Matthias, D., Juergen, G., Gabriele, F., and Luc, V.G. (2012, January 16\u201321). Real-time facial feature detection using conditional regression forests. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1016\/j.imavis.2005.07.009","article-title":"Generic vs. person specific active appearance models","volume":"23","author":"Rlph","year":"2005","journal-title":"Image Vis. Comput."},{"key":"ref_3","unstructured":"Syed, Z.G., and Ajmal, M. (2014, January 24\u201328). Perceptual differences between men and women: A 3D facial morphometric perspective. Proceedings of the IEEE 22nd International Conference on Pattern Recognition, Stockholm, Sweden."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TPAMI.2017.2781233","article-title":"HyperFace: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition","volume":"41","author":"Rajeev","year":"2019","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"ref_5","unstructured":"Ping-Han, L., Jui-Yu, H., and Yi-Ping, H. (2010, January 23\u201326). Automatic gender recognition using fusion of facial strips. Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey."},{"key":"ref_6","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":"Jordi","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","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":"Juan","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.patrec.2011.05.016","article-title":"Learning local binary patterns for gender classification on real-world face images","volume":"33","author":"Caifeng","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.patrec.2015.09.014","article-title":"On using periocular biometric for gender classification in the wild","volume":"82","author":"Modesto","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_10","unstructured":"Graham, D., Hayden, E., and John, S. (1981). Perceiving and Remembering Faces, Academic Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/JPROC.2006.884093","article-title":"Face recognition by humans: Nineteen results all computer vision researchers should know about","volume":"94","author":"Sinha","year":"2006","journal-title":"Proc. IEEE"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khan, K., Mauro, M., and Leonardi, R. (2015, January 27\u201330). Multi-class semantic segmentation of faces. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350915"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khan, K., Mauro, M., Migliorati, P., and Leonardi, R. (2017, January 10\u201314). Head pose estimation through multi-class face segmentation. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019521"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/34.1000244","article-title":"Learning gender with support faces","volume":"24","author":"Moghaddam","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s10044-015-0499-6","article-title":"A review of facial gender recognition","volume":"18","author":"Ng","year":"2015","journal-title":"Pattern Anal. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/TPAMI.2007.70800","article-title":"Evaluation of gender classification methods with automatically detected and aligned faces","volume":"30","author":"Makinen","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","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_18","first-page":"572","article-title":"Sexnet: A neural network identifies sex from human faces","volume":"3","author":"Golomb","year":"1990","journal-title":"Conf. Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11263-006-8910-9","article-title":"Boosting sex identification performance","volume":"71","author":"Baluja","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1007\/s00500-018-03679-5","article-title":"Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors","volume":"23","author":"Geetha","year":"2019","journal-title":"Soft Comput."},{"key":"ref_21","unstructured":"(2019, May 13). Centro Universitario da FEI, FEI Face Database. Available online: http:\/\/www.fei.edu.br\/~cet\/facedatabase.html."},{"key":"ref_22","first-page":"593","article-title":"Sexual dimorphism in the human face assessed by Euclidean distance matrix analysis","volume":"183","author":"Ferrario","year":"1993","journal-title":"J. Anat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1068\/p220153","article-title":"What\u2019s the difference between men and women? Evidence from facial measurement","volume":"22","author":"Burton","year":"1993","journal-title":"Perception"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1016\/S0042-6989(97)00010-2","article-title":"Gender discrimination and prediction on the basis of facial metric information","volume":"37","author":"Fellous","year":"1997","journal-title":"Vis. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1109\/TPAMI.2008.233","article-title":"Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion","volume":"31","author":"Toews","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TIFS.2014.2359646","article-title":"Age and gender estimation of unfiltered faces","volume":"9","author":"Eidinger","year":"2014","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1109\/TIP.2014.2331759","article-title":"Semantic pyramids for gender and action recognition","volume":"23","author":"Khan","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TPAMI.2014.2302443","article-title":"The hidden sides of names\u2014Face modeling with first name attributes","volume":"36","author":"Chen","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/TPAMI.2014.2362759","article-title":"Demographic estimation from face images: human vs. machine performance","volume":"37","author":"Han","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Levi, G., and Hassner, T. (2015, January 7\u201312). Age and gender classification using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.neucom.2017.08.062","article-title":"A hybrid deep learning CNN\u2013ELM for age and gender classification","volume":"275","author":"Duan","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_32","unstructured":"Ricanek, K., and Tesafaye, T. (2006, January 10\u201312). Morph: A longitudinal image database of normal adult age-progression. Proceedings of the Seventh International Conference on Automatic Face and Gesture Recognition, Southampton, UK."},{"key":"ref_33","unstructured":"Huang, G.B., Mattar, M., Berg, T., and Learned-Miller, E. (2007). Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, University of Massachusetts. Technical Report."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/S0262-8856(97)00070-X","article-title":"The FERET database and evaluation procedure for face-recognition algorithms","volume":"16","author":"Phillips","year":"1998","journal-title":"Image Vis. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single shot multibox detector. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_36","unstructured":"Lafferty, J., McCallum, A., and Pereira, F.C. (July, January 28). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001), Williamstown, MA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s11263-014-0744-2","article-title":"Seeds: Superpixels extracted via energy-driven sampling","volume":"111","author":"Boix","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1109\/TNNLS.2015.2441735","article-title":"Compound rank-k projections for bilinear analysis","volume":"27","author":"Chang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_39","unstructured":"Jerod, W. (2019, May 13). Code. Available online: http:\/\/www.cs.grinnell.edu\/ weinman\/code\/index.shtml."},{"key":"ref_40","first-page":"886","article-title":"Histograms of oriented gradients for human detection","volume":"1","author":"Dalal","year":"2005","journal-title":"Int. Conf. Comput. Vis. Pattern Recognit."},{"key":"ref_41","unstructured":"(2019, May 13). Alglib. Available online: www.alglib.net.5."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","unstructured":"Lapuschkin, S., Binder, A., Muller, K.R., and Samek, W. (2017, January 22\u201329). Understanding and comparing deep neural networks for age and gender classification. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hassner, T., Harel, S., Paz, E., and Enbar, R. (2015, January 7\u201312). Effective face frontalization in unconstrained images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299058"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jvcir.2016.11.002","article-title":"Gender dictionary learning for gender classification","volume":"42","author":"Moeini","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/TIFS.2013.2242063","article-title":"Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape","volume":"8","author":"Tapia","year":"2013","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jvlc.2014.10.016","article-title":"An illumination, expression, and noise invariant gender classifier using two-directional 2DPCA on real Gabor space","volume":"26","author":"Rai","year":"2015","journal-title":"J. Vis. Lang. Comput."},{"key":"ref_48","unstructured":"Afifi, M., and Abdelhamed, A. (2017). AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces. arXiv."},{"key":"ref_49","unstructured":"Thomaz, C., Giraldi, G., Costa, J., and Gillies, D. (2012). A priori-driven PCA. Asian Conference on Computer Vision, Springer."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Van de Wolfshaar, J., Karaaba, M.F., and Wiering, M.A. (2015, January 7\u201310). Deep convolutional neural networks and support vector machines for gender recognition. Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa.","DOI":"10.1109\/SSCI.2015.37"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kumar, N., Belhumeur, P., and Nayar, S. (2008, January 12\u201318). FaceTracer: A Search Engine for Large Collections of Images with Faces. Proceedings of the European Conference on Computer Vision (ECCV), Marseille, France.","DOI":"10.1007\/978-3-540-88693-8_25"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, N., Paluri, M., Ranzato, M.A., Darrell, T., and Bourdev, L. (2014, January 23\u201328). Panda: Pose aligned networks for deep attribute modeling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.212"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, J., and Zhang, Y. (2013, January 23\u201328). Learning surf cascade for fast and accurate object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.445"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T09:00:14Z","timestamp":1721379614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/6\/770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,6]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["sym11060770"],"URL":"https:\/\/doi.org\/10.3390\/sym11060770","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,6]]}}}
