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arXiv · 2610.06102

Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation

Fernando Alonso-Fernandez·Kevin Hernandez-Diaz·Jose Maria Buades·Josef Bigun
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Abstract

We investigate CLIP for zero-shot gender estimation from full-face and periocular images. Three CLIP backbones are evaluated on 11,299 frontal images from Adience using image-text similarity with male/female prompts, achieving 95.54% full-face accuracy without task-specific training. For periocular, zero-shot predictions are strongly biased towards males, primarily due to a misaligned decision boundary. Threshold alignment substantially reduces this bias, reaching 85.29% accuracy. Linear SVMs trained on CLIP features provide only marginal gains, with a best periocular accuracy of 86.17%, approximately 2.8% above previous Adience results in the literature. Nevertheless, the gap with full-face performance confirms the greater difficulty of periocular gender estimation

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Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose Maria Buades, Josef Bigun. 2026-10-05. Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation. https://arxiv.org/abs/2610.06102

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