# 11K Hands: Gender recognition and biometric identification using a large dataset of hand images

arXiv: https://arxiv.org/abs/1711.04322 · HTML: https://mlsanity.com/paper/1711.04322/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2017.

## Built on by (most cited first, 30 of 46)

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- [Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning](https://mlsanity.com/paper/2303.15263/) (2023)
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- [Efficient Palm-Line Segmentation with U-Net Context Fusion Module](https://mlsanity.com/paper/2102.12127/) (2021)
- [Hand gesture detection in tests performed by older adults](https://mlsanity.com/paper/2110.14461/) (2021)
- [Improving Hand Recognition in Uncontrolled and Uncooperative Environments using Multiple Spatial Transformers and Loss Functions](https://mlsanity.com/paper/2311.05383/) (2023)
- [Hi5: Synthetic Data for Inclusive, Robust, Hand Pose Estimation](https://mlsanity.com/paper/2406.03599/) (2024)
- [CLIP-HandID: Vision-Language Model for Hand-Based Person Identification](https://mlsanity.com/paper/2506.12447/) (2025)

## Abstract (arXiv)

The human hand possesses distinctive features which can reveal gender information. In addition, the hand is considered one of the primary biometric traits used to identify a person. In this work, we propose a large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification. Using this dataset, a convolutional neural network (CNN) can be trained effectively for the gender recognition task. Based on this, we design a two-stream CNN to tackle the gender recognition problem. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for the biometric identification task. We show that the dorsal side of hand images, captured by a regular digital camera, convey effective distinctive features similar to, if not better, those available in the palmar hand images. To facilitate access to the proposed dataset and replication of our experiments, the dataset, trained CNN models, and Matlab source code are available at (https://goo.gl/rQJndd).