On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition

Table 6

Fusion results based on FMR candidates.

(a) Fusions of ef_arcplus and Magface to improve skin color fairness
	FMR	FNMR	FMR	FMR	GARBE
	All	All	Dark	Light	()
ef_arcplus	0.1	0.753	0.519	0.066	0.773
Magface	0.1	0.389	0.080	0.195	0.418
AND-fusion	0.009	0.782	0.030	0.013	0.398
OR-fusion	0.191	0.361	0.570	0.249	0.392
Score-fusion	0.1	0.393	0.275	0.147	0.302
(b) Fusions of ef_cosplus and Magface to improve gender fairness
	FMR	FNMR	FMR	FMR	GARBE
	All	All	Female	Male	()
ef_cosplus	0.1	0.774	0.084	0.315	0.579
Magface	0.1	0.389	0.246	0.048	0.669
AND-fusion	0.007	0.804	0.014	0.014	0.006
OR-fusion	0.193	0.360	0.317	0.351	0.051
Score-fusion	0.1	0.395	0.194	0.158	0.102
(c) Fusions of af_casia, ef_rcplus, and Magface to improve fairness of demographic subgroups
	FMR	FNMR	FMR	FMR	FMR	FMR	GARBE
	All	All	df	dm	lf	lm	( = 1)
af_casia	0.1	4.341	1.353	0.562	0.122	0.017	0.720
ef_arcplus	0.1	0.753	1.360	0.578	0.083	0.108	0.672
Magface	0.1	0.389	0.551	0.086	0.278	0.047	0.588
AND-fusion	0.002	4.431	0.068	0.010	0.004	0.001	0.842
OR-fusion	0.277	0.349	2.758	1.114	0.447	0.166	0.628
Majority-Vote	0.021	0.704	0.441	0.104	0.034	0.008	0.778
Score-fusion	0.1	0.408	1.400	0.357	0.201	0.044	0.702

Bold numbers mark best results.