{"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/ghostfacenets-lightweight-face-recognition","title":"GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations","arxiv_id":null,"date":"2023-04-10","proceeding":"IEEE Access 2023 4","authors":["Mohamad Alansari","Oussama Abdul Hay","Sajid Javed","Abdulhadi Shoufan","Yahya Zweiri","Naoufel Werghi"],"abstract":"The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules represents a major innovation in this area. Ghost modules use a series of inexpensive linear transformations to extract additional feature maps from a set of intrinsic features, allowing for a more comprehensive representation of the underlying information. GhostNetV1 and GhostNetV2, both of which are based on Ghost modules, serve as the foundation for a group of lightweight face recognition models called GhostFaceNets. GhostNetV2 expands upon the original GhostNetV1 by adding an attention mechanism to capture long-range dependencies. Evaluation of GhostFaceNets using various benchmarks reveals that these models offer superior performance while requiring a computational complexity of approximately 60–275 MFLOPs. This is significantly lower than that of State-Of-The-Art (SOTA) big convolutional neural network (CNN) models, which can require hundreds of millions of FLOPs. GhostFaceNets trained with the ArcFace loss on the refined MS-Celeb-1M dataset demonstrate SOTA performance on all benchmarks. In comparison to previous SOTA mobile CNNs, GhostFaceNets greatly improve efficiency for face verification tasks. The GhostFaceNets code is available at: https://github.com/HamadYA/GhostFaceNets .","url_abs":"https://ieeexplore.ieee.org/document/10098610","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10098610","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":"ghostfacenets-lightweight-face-recognition","repo_url":"https://github.com/HamadYA/GhostFaceNets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ghostfacenets-lightweight-face-recognition","repo_url":"https://github.com/Armxyz1/Results-on-RFW","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"ghostfacenets-lightweight-face-recognition","repo_url":"https://github.com/FaceOnLive/Face-Recognition-SDK-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ghostfacenets-lightweight-face-recognition","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":"ghostfacenets-lightweight-face-recognition","repo_url":"https://github.com/Hazqeel09/ellzaf_ml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"ghostfacenets-lightweight-face-recognition","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"}},{"paper_slug":"ghostfacenets-lightweight-face-recognition","repo_url":"https://github.com/serengil/deepface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"face-identification","task_name":"Face Identification"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"lightweight-face-recognition","task_name":"Lightweight Face Recognition"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"arcface","method_name":"ArcFace"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-identification-on-megaface","task":"Face Identification","dataset":"MegaFace","model":"GhostFaceNetV2-1","rank_in_archive_order":5,"of":13,"metrics":{"Accuracy":"98.64%"},"uses_additional_data":false},{"leaderboard":"/sota/face-recognition-on-calfw","task":"Face Recognition","dataset":"CALFW","model":"GhostFaceNetV2-1","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"0.9612"},"uses_additional_data":false},{"leaderboard":"/sota/face-recognition-on-cfp-ff","task":"Face Recognition","dataset":"CFP-FF","model":"GhostFaceNetV2-1","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"99.9143"},"uses_additional_data":false},{"leaderboard":"/sota/face-recognition-on-cfp-fp","task":"Face Recognition","dataset":"CFP-FP","model":"GhostFaceNetV2-1","rank_in_archive_order":1,"of":8,"metrics":{"Accuracy":"0.9933"},"uses_additional_data":false},{"leaderboard":"/sota/face-recognition-on-cplfw","task":"Face Recognition","dataset":"CPLFW","model":"GhostFaceNetV2-1","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"0.9465"},"uses_additional_data":false},{"leaderboard":"/sota/face-recognition-on-lfw","task":"Face Recognition","dataset":"LFW","model":"GhostFaceNetV2-1 (MS1MV3)","rank_in_archive_order":1,"of":16,"metrics":{"Accuracy":"0.998667"},"uses_additional_data":true},{"leaderboard":"/sota/face-verification-on-agedb-30","task":"Face Verification","dataset":"AgeDB-30","model":"GhostFaceNetV2-1","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy":"0.9862"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-megaface","task":"Face Verification","dataset":"MegaFace","model":"GhostFaceNetV2-1","rank_in_archive_order":3,"of":12,"metrics":{"Accuracy":"98.72%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}