{"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/faceqnet-quality-assessment-for-face","title":"FaceQnet: Quality Assessment for Face Recognition based on Deep Learning","arxiv_id":"1904.01740","date":"2019-04-03","proceeding":null,"authors":["Javier Hernandez-Ortega","Javier Galbally","Julian Fierrez","Rudolf Haraksim","Laurent Beslay"],"abstract":"In this paper we develop a Quality Assessment approach for face recognition\nbased on deep learning. The method consists of a Convolutional Neural Network,\nFaceQnet, that is used to predict the suitability of a specific input image for\nface recognition purposes. The training of FaceQnet is done using the VGGFace2\ndatabase. We employ the BioLab-ICAO framework for labeling the VGGFace2 images\nwith quality information related to their ICAO compliance level. The\ngroundtruth quality labels are obtained using FaceNet to generate comparison\nscores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making\nit capable of returning a numerical quality measure for each input image.\nFinally, we verify if the FaceQnet scores are suitable to predict the expected\nperformance when employing a specific image for face recognition with a COTS\nface recognition system. Several conclusions can be drawn from this work, most\nnotably: 1) we managed to employ an existing ICAO compliance framework and a\npretrained CNN to automatically label data with quality information, 2) we\ntrained FaceQnet for quality estimation by fine-tuning a pre-trained face\nrecognition network (ResNet-50), and 3) we have shown that the predictions from\nFaceQnet are highly correlated with the face recognition accuracy of a\nstate-of-the-art commercial system not used during development. FaceQnet is\npublicly available in GitHub.","url_abs":"http://arxiv.org/abs/1904.01740v2","url_pdf":"http://arxiv.org/pdf/1904.01740v2.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":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/uam-biometrics/FaceQnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/Alireza-Akhavan/face-quality-metrics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/javier-hernandezo/faceqgen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/javier-hernandezo/faceqnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/uam-biometrics/faceqgen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"faceqnet-quality-assessment-for-face","repo_url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"face-recognition","task_name":"Face Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.01740","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}