Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

2 Sep 2018 • Jian Zhao • Yu Cheng • Yi Cheng • Yang Yang • Haochong Lan • Fang Zhao • Lin Xiong • Yan Xu • Jianshu Li • Sugiri Pranata • ShengMei Shen • Junliang Xing • Hengzhu Liu • Shuicheng Yan • Jiashi Feng

Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intra-class variations... (read more)

PDF Abstract

Tasks
Add Remove

TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Age-Invariant Face Recognition CACDVS AIM Accuracy 99.38% # 3
Compare
Age-Invariant Face Recognition CACDVS AIM + CAFR Accuracy 99.76% # 1
Compare
Age-Invariant Face Recognition CAFR AIM Accuracy 84.81% # 1
Compare
Age-Invariant Face Recognition FG-NET AIM Accuracy 93.20% # 1
Compare
Face Verification IJB-C AIM TAR @ FAR=0.01 93.50% # 3
Compare
Age-Invariant Face Recognition MORPH Album2 AIM Rank-1 Recognition Rate 99.13% # 2
Compare
Age-Invariant Face Recognition MORPH Album2 AIM + CAFR Rank-1 Recognition Rate 99.65% # 1
Compare

Methods used in the Paper
Add Remove

METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet
Contact us on: [email protected]. Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Privacy Cookies policy