DeAndres-Tame, IvanIvanDeAndres-TameTolosana, RubenRubenTolosanaMelzi, PietroPietroMelziVera-Rodriguez, RubenRubenVera-RodriguezKim, MinchulMinchulKimRathgeb, ChristianChristianRathgebLiu, XiaomingXiaomingLiuMorales, AythamiAythamiMoralesFierrez, JulianJulianFierrezOrtega-Garcia, JavierJavierOrtega-GarciaZhong, ZhizhouZhizhouZhongHuang, YugeYugeHuangMi, YuxiYuxiMiDing, ShouhongShouhongDingZhou, ShuigengShuigengZhouHe, ShuaiShuaiHeFu, LingzhiLingzhiFuCong, HengHengCongZhang, RongyuRongyuZhangXiao, ZhihongZhihongXiaoSmirnov, EvgenyEvgenySmirnovPimenov, AntonAntonPimenovGrigorev, AlekseiAlekseiGrigorevTimoshenko, DenisDenisTimoshenkoAsfaw, Kaleb MesfinKaleb MesfinAsfawYaw Low, ChengChengYaw LowLiu, HaoHaoLiuWang, ChuyiChuyiWangZuo, QingQingZuoHe, ZhixiangZhixiangHeShahreza, Hatef OtroshiHatef OtroshiShahrezaGeorge, AnjithAnjithGeorgeUnnervik, AlexanderAlexanderUnnervikRahimi, ParsaParsaRahimiMarcel, SébastienSébastienMarcelNeto, Pedro C.Pedro C.NetoHuber, MarcoMarcoHuberKolf, Jan NiklasJan NiklasKolfDamer, NaserNaserDamerBoutros, FadiFadiBoutrosCardoso, Jaime S.Jaime S.CardosoSequeira, Ana F.Ana F.SequeiraAndrea AtzoriFenu, GianniGianniFenuMarras, MirkoMirkoMarrasŠtruc, VitomirVitomirŠtrucYu, JiangJiangYuLi, ZhangjieZhangjieLiLi, JichunJichunLiZhao, WeisongWeisongZhaoLei, ZhenZhenLeiZhu, XiangyuXiangyuZhuZhang, Xiao-YuXiao-YuZhangBiesseck, BernardoBernardoBiesseckVidal, PedroPedroVidalCoelho, LuizLuizCoelhoGranada, RogerRogerGranadaMenotti, DavidDavidMenotti2024-10-012024-10-012024https://publica.fraunhofer.de/handle/publica/47593910.1109/CVPRW63382.2024.00323Synthetic data is gaining increasing relevance for train ing machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra class variability, time and errors produced in manual la beling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to in vestigate the use of synthetic data in face recognition to ad dress current technological limitations, including data pri vacy concerns, demographic biases, generalization to novel scenarios, and performance constraints in challenging sit uations such as aging, pose variations, and occlusions. Un like the 1st edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2nd edition we propose new sub tasks that allow participants to explore novel face genera tive methods. The outcomes of the 2nd FRCSyn Challenge, along with the proposed experimental protocol and bench marking contribute significantly to the application of syn thetic data to face recognition.enBranche: Information TechnologyResearch Line: Computer vision (CV)Research Line: Human computer interaction (HCI)Research Line: Machine learning (ML)LTA: Interactive decision-making support and assistance systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)LTA: Generation, capture, processing, and output of images and 3D modelsBiometricsFace recognitionMachine learningATHENEconference paper