Does Face Recognition Error Echo Gender Classification Error?

• URL: http://arxiv.org/abs/2104.13803v1
• Date: Wed, 28 Apr 2021 14:43:31 GMT
• Title: Does Face Recognition Error Echo Gender Classification Error?
• Authors: Ying Qiu, V\'itor Albiero, Michael C. King, Kevin W. Bowyer
• Abstract summary: We analyze results from three different gender classification algorithms, and two face recognition algorithms. For impostor image pairs, our results show that pairs in which one image has a gender classification error have a better impostor distribution. For genuine image pairs, our results show that individuals whose images have a mix of correct and incorrect gender classification have a worse genuine distribution.
• Score: 9.176056742068813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper is the first to explore the question of whether images that are classified incorrectly by a face analytics algorithm (e.g., gender classification) are any more or less likely to participate in an image pair that results in a face recognition error. We analyze results from three different gender classification algorithms (one open-source and two commercial), and two face recognition algorithms (one open-source and one commercial), on image sets representing four demographic groups (African-American female and male, Caucasian female and male). For impostor image pairs, our results show that pairs in which one image has a gender classification error have a better impostor distribution than pairs in which both images have correct gender classification, and so are less likely to generate a false match error. For genuine image pairs, our results show that individuals whose images have a mix of correct and incorrect gender classification have a worse genuine distribution (increased false non-match rate) compared to individuals whose images all have correct gender classification. Thus, compared to images that generate correct gender classification, images that generate gender classification errors do generate a different pattern of recognition errors, both better (false match) and worse (false non-match).

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