{"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/facial-key-points-detection-using-deep","title":"Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet","arxiv_id":"1710.00977","date":"2017-10-03","proceeding":null,"authors":["Naimish Agarwal","Artus Krohn-Grimberghe","Ranjana Vyas"],"abstract":"Facial Key Points (FKPs) Detection is an important and challenging problem in\nthe fields of computer vision and machine learning. It involves predicting the\nco-ordinates of the FKPs, e.g. nose tip, center of eyes, etc, for a given face.\nIn this paper, we propose a LeNet adapted Deep CNN model - NaimishNet, to\noperate on facial key points data and compare our model's performance against\nexisting state of the art approaches.","url_abs":"http://arxiv.org/abs/1710.00977v1","url_pdf":"http://arxiv.org/pdf/1710.00977v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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