# Deep Learning in Palmprint Recognition-A Comprehensive Survey

arXiv: https://arxiv.org/abs/2501.01166 · HTML: https://mlsanity.com/paper/2501.01166/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 172 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 27 of 27)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Comprehensive Exploration of Synthetic Data Generation: A Survey](https://mlsanity.com/paper/2401.02524/) (2024)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Synthetic Data for Face Recognition: Current State and Future Prospects](https://mlsanity.com/paper/2305.01021/) (2023)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Palmprint Recognition in Uncontrolled and Uncooperative Environment](https://mlsanity.com/paper/1911.12514/) (2019)
- [Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography](https://mlsanity.com/paper/2308.16290/) (2023)
- [RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition](https://mlsanity.com/paper/2307.14016/) (2023)
- [Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification](https://mlsanity.com/paper/2308.00451/) (2023)
- [Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings](https://mlsanity.com/paper/2401.08111/) (2024)
- [BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion](https://mlsanity.com/paper/2110.01179/) (2021)
- [A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage](https://mlsanity.com/paper/2403.02680/) (2024)
- [Cross-Chirality Palmprint Verification: Left is Right for the Right Palmprint](https://mlsanity.com/paper/2409.13056/) (2024)
- [Towards Unconstrained Palmprint Recognition on Consumer Devices: a Literature Review](https://mlsanity.com/paper/2003.00737/) (2020)

## Built on by (most cited first, 5 of 5)

- [PalmBridge: A Plug-and-Play Feature Alignment Framework for Open-Set Palmprint Verification](https://mlsanity.com/paper/2601.20351/) (2026)
- [X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication](https://mlsanity.com/paper/2606.08437/) (2026)
- [FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint Verification](https://mlsanity.com/paper/2503.04837/) (2025)
- [CAAP: Capture-Aware Adversarial Patch Attacks on Palmprint Recognition Models](https://mlsanity.com/paper/2604.06987/) (2026)
- [BIO-MEMART: Biometric-Aware KV Cache Memory for Multi-User LLM Agents](https://mlsanity.com/paper/2609.08566/) (2026)

## Abstract (arXiv)

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.