# One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations

arXiv: https://arxiv.org/abs/2307.05128 · HTML: https://mlsanity.com/paper/2307.05128/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [A Survey on Periocular Biometrics Research](https://mlsanity.com/paper/1810.03360/) (2018)
- [Periocular Recognition Using CNN Features Off-the-Shelf](https://mlsanity.com/paper/1809.06157/) (2018)
- [Ocular Recognition Databases and Competitions: A Survey](https://mlsanity.com/paper/1911.09646/) (2019)
- [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)
- [Cross-Sensor Periocular Biometrics in a Global Pandemic: Comparative Benchmark and Novel Multialgorithmic Approach](https://mlsanity.com/paper/1902.08123/) (2019)
- [SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms](https://mlsanity.com/paper/2007.08566/) (2020)
- [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)
- [Deep Representations for Cross-spectral Ocular Biometrics](https://mlsanity.com/paper/1911.09509/) (2019)
- [Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition](https://mlsanity.com/paper/2111.01325/) (2021)
- [The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments](https://mlsanity.com/paper/1808.10032/) (2018)
- [One-shot Representational Learning for Joint Biometric and Device Authentication](https://mlsanity.com/paper/2101.00524/) (2021)

## Built on by (most cited first, 4 of 4)

- [Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition](https://mlsanity.com/paper/2407.19472/) (2024)
- [Understanding and Improving CNNs with Complex Structure Tensor: A Biometrics Study](https://mlsanity.com/paper/2404.15608/) (2024)
- [Leveraging Large-Scale Face Datasets for Deep Periocular Recognition via Ocular Cropping](https://mlsanity.com/paper/2510.26294/) (2025)
- [Explicit Over Implicit: Enhancing CNNs Via Complex Structure Tensor Representations for Periocular Recognition](https://mlsanity.com/paper/2607.15410/) (2026)

## Abstract (arXiv)

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task. We analyze the outputs of CNN layers as identity-representing feature vectors. We examine the impact of Domain Adaptation on the network layers' output for unseen data and evaluate the method's robustness concerning data normalization and generalization of the best-performing layer. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images and fine-tuned for the target periocular dataset by utilizing out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard computer vision algorithms. For example, for the Cross-Eyed dataset, we could reduce the EER by 67% and 79% (from 1.70% and 3.41% to 0.56% and 0.71%) in the Close-World and Open-World protocols, respectively, for the periocular case. We also demonstrate that traditional algorithms like SIFT can outperform CNNs in situations with limited data or scenarios where the network has not been trained with the test classes like the Open-World mode. SIFT alone was able to reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for Cross-Eyed in the Close-World and Open-World protocols, respectively, and a reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the Open-World and single biometric case.