# Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition

arXiv: https://arxiv.org/abs/2407.19472 · HTML: https://mlsanity.com/paper/2407.19472/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 28 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [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)
- [Iris Recognition Based on SIFT Features](https://mlsanity.com/paper/2111.00176/) (2021)
- [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)
- [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)
- [Deep Network Pruning: A Comparative Study on CNNs in Face Recognition](https://mlsanity.com/paper/2405.18302/) (2024)

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

- [A Comparative Study of Vision Transformers and CNNs for Few-Shot Rigid Transformation and Fundamental Matrix Estimation](https://mlsanity.com/paper/2510.04794/) (2025)
- [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)
- [Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation](https://mlsanity.com/paper/2610.06102/) (2026)

## Abstract (arXiv)

We apply pre-trained architectures, originally developed for the ImageNet Large Scale Visual Recognition Challenge, for periocular recognition. These architectures have demonstrated significant success in various computer vision tasks beyond the ones for which they were designed. This work builds on our previous study using off-the-shelf Convolutional Neural Network (CNN) and extends it to include the more recently proposed Vision Transformers (ViT). Despite being trained for generic object classification, middle-layer features from CNNs and ViTs are a suitable way to recognize individuals based on periocular images. We also demonstrate that CNNs and ViTs are highly complementary since their combination results in boosted accuracy. In addition, we show that a small portion of these pre-trained models can achieve good accuracy, resulting in thinner models with fewer parameters, suitable for resource-limited environments such as mobiles. This efficiency improves if traditional handcrafted features are added as well.