2023/04/17 by Knoche, Martin, Rigoll, Gerhard · 2 citations
Computer Science · Engineering · #Artificial intelligence #Benchmark (surveying) #Code (set theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #Domain Adaptation and Few-Shot Learning #Engineering #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #Face (sociological concept) #Face and Expression Recognition #Face recognition and analysis #Facial recognition system #Human–machine system #Machine Learning (cs.LG) #Machine learning #Pattern recognition (psychology) #Programming language #Task (project management)
paper · pdf · doi:10.48550/arxiv.2304.08134
published in arXiv (Cornell University) (Cornell University)
Nowadays, face recognition systems surpass human performance on several datasets. However, there are still edge cases that the machine can't correctly classify. This paper investigates the effect of a combination of machine and human operators in the face verification task. First, we look closer at the edge cases for several state-of-the-art models to discover common datasets' challenging settings. Then, we conduct a study with 60 participants on these selected tasks with humans and provide an extensive analysis. Finally, we demonstrate that combining machine and human decisions can further improve the performance of state-of-the-art face verification systems on various benchmark datasets. Code and data are publicly available on GitHub.