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Face morphing attacks can compromise Face Recognition System (FRS) byexploiting their vulnerability. Face Morphing Attack Detection (MAD) techniqueshave been developed in recent past to deter such attacks and mitigate risksfrom morphing attacks. MAD algorithms, as any other algorithms should treat theimages of subjects from different ethnic origins in an equal manner and providenon-discriminatory results. While the promising MAD algorithms are tested forrobustness, there is no study comprehensively bench-marking their behaviouragainst various ethnicities. In this paper, we study and present acomprehensive analysis of algorithmic fairness of the existing Singleimage-based Morph Attack Detection (S-MAD) algorithms. We attempt to betterunderstand the influence of ethnic bias on MAD algorithms and to this extent,we study the performance of MAD algorithms on a newly created datasetconsisting of four different ethnic groups. With Extensive experiments usingsix different S-MAD techniques, we first present benchmark of detectionperformance and then measure the quantitative value of the algorithmic fairnessfor each of them using Fairness Discrepancy Rate (FDR). The results indicatethe lack of fairness on all six different S-MAD methods when trained and testedon different ethnic groups suggesting the need for reliable MAD approaches tomitigate the algorithmic bias.

Algorithmic Fairness in Face Morphing Attack Detection