From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments
Gender recognition from pedestrian imagery is acknowledged by many as a quasi-solved problem, yet most existing approaches evaluate performance in a within-domain setting, i.e., when the test and training data, though disjoint, closely resemble each other. This work provides the first exhaustive cro...
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MDPI AG
2025-07-01
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| author | Nzakiese Mbongo Kailash A. Hambarde Hugo Proença |
| author_facet | Nzakiese Mbongo Kailash A. Hambarde Hugo Proença |
| author_sort | Nzakiese Mbongo |
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| description | Gender recognition from pedestrian imagery is acknowledged by many as a quasi-solved problem, yet most existing approaches evaluate performance in a within-domain setting, i.e., when the test and training data, though disjoint, closely resemble each other. This work provides the first exhaustive cross-domain assessment of six architectures considered to represent the state of the art: ALM, VAC, Rethinking, LML, YinYang-Net, and MAMBA, across three widely known benchmarks: <span style="font-variant: small-caps;">PA-100K</span>, <span style="font-variant: small-caps;">PETA</span>, and <span style="font-variant: small-caps;">RAP</span>. All train/test combinations between datasets were evaluated, yielding 54 comparable experiments. The results revealed a performance split: median in-domain F1 approached 90% in most models, while the average drop under domain shift was up to 16.4 percentage points, with the most recent approaches degrading the most. The adaptive-masking ALM achieved an F1 above 80% in most transfer scenarios, particularly those involving high-resolution or pose-stable domains, highlighting the importance of strong inductive biases over architectural novelty alone. Further, to characterize robustness quantitatively, we introduced the <i>Unified Robustness Metric</i> (URM), which integrates the average cross-domain degradation performance into a single score. A qualitative saliency analysis also corroborated the numerical findings by exposing over-confidence and contextual bias in misclassifications. Overall, this study suggests that challenges in gender recognition are much more evident in cross-domain settings than under the commonly reported within-domain context. Finally, we formalize an open evaluation protocol that can serve as a baseline for future works of this kind. |
| format | Article |
| id | doaj-art-a4ed1558deba4f908ba2f20640e5e4e7 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-a4ed1558deba4f908ba2f20640e5e4e72025-08-20T03:16:56ZengMDPI AGSensors1424-82202025-07-012513416110.3390/s25134161From One Domain to Another: The Pitfalls of Gender Recognition in Unseen EnvironmentsNzakiese Mbongo0Kailash A. Hambarde1Hugo Proença2Department of Computer Science, University of Beira Interior, 6201-001 Covilhã, PortugalDepartment of Computer Science, University of Beira Interior, 6201-001 Covilhã, PortugalDepartment of Computer Science, University of Beira Interior, 6201-001 Covilhã, PortugalGender recognition from pedestrian imagery is acknowledged by many as a quasi-solved problem, yet most existing approaches evaluate performance in a within-domain setting, i.e., when the test and training data, though disjoint, closely resemble each other. This work provides the first exhaustive cross-domain assessment of six architectures considered to represent the state of the art: ALM, VAC, Rethinking, LML, YinYang-Net, and MAMBA, across three widely known benchmarks: <span style="font-variant: small-caps;">PA-100K</span>, <span style="font-variant: small-caps;">PETA</span>, and <span style="font-variant: small-caps;">RAP</span>. All train/test combinations between datasets were evaluated, yielding 54 comparable experiments. The results revealed a performance split: median in-domain F1 approached 90% in most models, while the average drop under domain shift was up to 16.4 percentage points, with the most recent approaches degrading the most. The adaptive-masking ALM achieved an F1 above 80% in most transfer scenarios, particularly those involving high-resolution or pose-stable domains, highlighting the importance of strong inductive biases over architectural novelty alone. Further, to characterize robustness quantitatively, we introduced the <i>Unified Robustness Metric</i> (URM), which integrates the average cross-domain degradation performance into a single score. A qualitative saliency analysis also corroborated the numerical findings by exposing over-confidence and contextual bias in misclassifications. Overall, this study suggests that challenges in gender recognition are much more evident in cross-domain settings than under the commonly reported within-domain context. Finally, we formalize an open evaluation protocol that can serve as a baseline for future works of this kind.https://www.mdpi.com/1424-8220/25/13/4161gender recognitionpedestrian attribute recognitionsoft biometricsmodel generalizationcomputer visiondeep learning |
| spellingShingle | Nzakiese Mbongo Kailash A. Hambarde Hugo Proença From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments Sensors gender recognition pedestrian attribute recognition soft biometrics model generalization computer vision deep learning |
| title | From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments |
| title_full | From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments |
| title_fullStr | From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments |
| title_full_unstemmed | From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments |
| title_short | From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments |
| title_sort | from one domain to another the pitfalls of gender recognition in unseen environments |
| topic | gender recognition pedestrian attribute recognition soft biometrics model generalization computer vision deep learning |
| url | https://www.mdpi.com/1424-8220/25/13/4161 |
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