Fine alignment, flexible fusion: A novel framework of multi-model score fusion in face identification

Abstract

Multi-model score fusion was considered a bottleneck problem in forensic face identification. While the score distribution of different face models varies greatly, the existing score processing methods cannot achieve accurate alignment. This paper proposed a score fusion framework named fine alignment and flexible fusion framework (FAFF). In FAFF, we took score-based likelihood ratios as the reference values to align the similarity scores generated by different face models. First, we set up a unified calibration test workflow based on the forensic likelihood ratio test. Then, 3 LLR anchor-based methods (LLRBA1, LLRBA2, and LLRBA3) and LLR curve-based methods (LLRBC) were proposed. Finally, we conducted fusion experiments on four face models (VGGface, Facenet, Arcface, and SFace). The experimental results show that on the CelebA dataset, compared with the existing MOEBA and PAN methods, LLRBC increased the TPR@ 10−7 FPR by 175.4 % and 162.9 %, and LLRBA increased by 55.6 % and 48.5 %.