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

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

Methods used in the Paper


METHOD TYPE
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