{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-fusion-based-gender-recognition-method","title":"A Fusion-based Gender Recognition Method Using Facial Images","arxiv_id":"1711.06451","date":"2017-11-17","proceeding":null,"authors":["Benyamin Ghojogh","Saeed Bagheri Shouraki","Hoda Mohammadzade","Ensieh Iranmehr"],"abstract":"This paper proposes a fusion-based gender recognition method which uses\nfacial images as input. Firstly, this paper utilizes pre-processing and a\nlandmark detection method in order to find the important landmarks of faces.\nThereafter, four different frameworks are proposed which are inspired by\nstate-of-the-art gender recognition systems. The first framework extracts\nfeatures using Local Binary Pattern (LBP) and Principal Component Analysis\n(PCA) and uses back propagation neural network. The second framework uses Gabor\nfilters, PCA, and kernel Support Vector Machine (SVM). The third framework uses\nlower part of faces as input and classifies them using kernel SVM. The fourth\nframework uses Linear Discriminant Analysis (LDA) in order to classify the side\noutline landmarks of faces. Finally, the four decisions of frameworks are fused\nusing weighted voting. This paper takes advantage of both texture and\ngeometrical information, the two dominant types of information in facial gender\nrecognition. Experimental results show the power and effectiveness of the\nproposed method. This method obtains recognition rate of 94% for neutral faces\nof FEI face dataset, which is equal to state-of-the-art rate for this dataset.","url_abs":"http://arxiv.org/abs/1711.06451v1","url_pdf":"http://arxiv.org/pdf/1711.06451v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-fusion-based-gender-recognition-method","repo_url":"https://github.com/bghojogh/Face-Gender-Recognition-Fusion","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"age-and-gender-classification","task_name":"Age And Gender Classification"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"gender-prediction","task_name":"Gender Prediction"}],"methods":[{"method_slug":"pca","method_name":"PCA"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}