# Periocular Recognition Using CNN Features Off-the-Shelf

arXiv: https://arxiv.org/abs/1809.06157 · HTML: https://mlsanity.com/paper/1809.06157/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 13 of 13)

- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (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)
- [One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations](https://mlsanity.com/paper/2307.05128/) (2023)
- [An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification](https://mlsanity.com/paper/2307.13428/) (2023)
- [Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition](https://mlsanity.com/paper/2407.19472/) (2024)
- [Selfie Periocular Verification using an Efficient Super-Resolution Approach](https://mlsanity.com/paper/2102.08449/) (2021)
- [One-shot Representational Learning for Joint Biometric and Device Authentication](https://mlsanity.com/paper/2101.00524/) (2021)
- [Segmentation-free Direct Iris Localization Networks](https://mlsanity.com/paper/2210.10403/) (2022)
- [Writer Recognition Using Off-line Handwritten Single Block Characters](https://mlsanity.com/paper/2201.10665/) (2022)
- [Synthesis-Guided Feature Learning for Cross-Spectral Periocular Recognition](https://mlsanity.com/paper/2111.08738/) (2021)
- [Exploring Complementarity and Explainability in CNNs for Periocular Verification Across Acquisition Distances](https://mlsanity.com/paper/2510.26282/) (2025)
- [Leveraging Large-Scale Face Datasets for Deep Periocular Recognition via Ocular Cropping](https://mlsanity.com/paper/2510.26294/) (2025)

## Abstract (arXiv)

Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these off-the-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to ~40%, with the fusion of CNN and traditional features providing additional improvements.