# Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation

arXiv: https://arxiv.org/abs/2610.06102 · HTML: https://mlsanity.com/paper/2610.06102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MiVOLO: Multi-input Transformer for Age and Gender Estimation](https://mlsanity.com/paper/2307.04616/) (2023)
- [How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability](https://mlsanity.com/paper/2401.13641/) (2024)
- [FaceXBench: Evaluating Multimodal LLMs on Face Understanding](https://mlsanity.com/paper/2501.10360/) (2025)
- [ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities](https://mlsanity.com/paper/2403.02965/) (2024)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Benchmarking Foundation Models for Zero-Shot Biometric Tasks](https://mlsanity.com/paper/2505.24214/) (2025)
- [Face-MLLM: A Large Face Perception Model](https://mlsanity.com/paper/2410.20717/) (2024)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [Periocular Biometrics: A Modality for Unconstrained Scenarios](https://mlsanity.com/paper/2212.13792/) (2022)
- [Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images](https://mlsanity.com/paper/2103.16760/) (2021)
- [AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition](https://mlsanity.com/paper/2407.01332/) (2024)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition](https://mlsanity.com/paper/2407.19472/) (2024)
- [Recognizing Gender from Human Facial Regions using Genetic Algorithm](https://mlsanity.com/paper/1712.01661/) (2017)
- [Deep Network Pruning: A Comparative Study on CNNs in Face Recognition](https://mlsanity.com/paper/2405.18302/) (2024)
- [FaceInsight: A Multimodal Large Language Model for Face Perception](https://mlsanity.com/paper/2504.15624/) (2025)
- [A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images](https://mlsanity.com/paper/2403.12483/) (2024)

## Abstract (arXiv)

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