{"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/convolutional-neural-networks-for-facial","title":"Convolutional Neural Networks for Facial Expression Recognition","arxiv_id":"1704.06756","date":"2017-04-22","proceeding":null,"authors":["Shima Alizadeh","Azar Fazel"],"abstract":"We have developed convolutional neural networks (CNN) for a facial expression\nrecognition task. The goal is to classify each facial image into one of the\nseven facial emotion categories considered in this study. We trained CNN models\nwith different depth using gray-scale images. We developed our models in Torch\nand exploited Graphics Processing Unit (GPU) computation in order to expedite\nthe training process. In addition to the networks performing based on raw pixel\ndata, we employed a hybrid feature strategy by which we trained a novel CNN\nmodel with the combination of raw pixel data and Histogram of Oriented\nGradients (HOG) features. To reduce the overfitting of the models, we utilized\ndifferent techniques including dropout and batch normalization in addition to\nL2 regularization. We applied cross validation to determine the optimal\nhyper-parameters and evaluated the performance of the developed models by\nlooking at their training histories. We also present the visualization of\ndifferent layers of a network to show what features of a face can be learned by\nCNN models.","url_abs":"http://arxiv.org/abs/1704.06756v1","url_pdf":"http://arxiv.org/pdf/1704.06756v1.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":"convolutional-neural-networks-for-facial","repo_url":"https://github.com/jaydeepthik/kaggle-facial-expression-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"l2-regularization","task_name":"L2 Regularization"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}