---
title: "Open Palm Hand Images Dataset"
description: "This high-quality open palm dataset includes 179,052 annotated images collected from 29,842 people, with each set containing six palm photos, two printed-hand images, and two…"
url: "https://unidata.pro/datasets/open-palm-hand-images/"
date_modified: "2026-05-26T08:03:22+03:00"
language: "en-US"
---
This high-quality open palm dataset includes 179,052 annotated images collected from 29,842 people, with each set containing six palm photos, two printed-hand images, and two replay videos. Designed for hand recognition and computer vision research, it provides detailed metadata - age, gender, ethnicity, profession, device type, dominant hand, and jewelry status.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 179,052 — **Text:** Images
- **Number:** 29,842 — **Text:** People

### Tooltips Section

**Tooltip items:**

- **Name:** Image Processing
- **Name:** Machine Learning
- **Name:** Hand Recognition
- **Name:** Forensics
- **Name:** Computer Vision

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Open palm images designed for training and evaluating hand-based identification algorithms |
| Data types | Image |
| Tasks | Hand Recognition, Computer Vision |
| Total number of files | 179,052 |
| Number of people | 29,842 |
| Number of files in a set | 10 images (6 hand, 2 printed hand, 2 replay) |
| Labeling | Metadata (Age, gender, ethnicity, profession (or previous job), device, dominant hand, has_jewelry ) |
| Gender | Male, Female |

**Media Slider:**

- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/10/palm-dataset-1.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/10/palm-dataset2-1.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/10/palm-dataset3-e1764684790964.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/13a4g7no-2kFA57cAVsj7rPm9dO3mugoQ)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image Extensions | JPG |

**Source and data collection methodology:** Source and collection methodology: Data was collected via crowdsourcing platforms.

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Biometric Authentication and Security — **Title:** Enhancing Palm Recognition and Hand Verification Systems — **Text:** Open Palm Hand Images Dataset supports the development of palm recognition and hand detection systems used in biometric authentication. With high-quality human palm images of both right and left hands, it provides reliable training data for model training, helping improve accuracy and security in identity verification and access control applications.
- **Industry:** Gesture Recognition and Human-Computer Interaction — **Title:** Training AI Models for Gesture-Based Interfaces — **Text:** This hand dataset enables gesture recognition research by offering thousands of annotated hand poses and palm movements. It helps train deep learning models to interpret hand gestures, supporting applications in augmented reality, gaming, and touchless device control, where accurate pose estimation and motion tracking are essential.
- **Industry:** Robotics and Motion Analysis — **Title:** Improving Robotic Perception and Manipulation Systems — **Text:** The palm dataset provides a valuable foundation for robotic vision systems to analyze hand movements and keypoints. By using this dataset comprising diverse palm images captured from multiple angles, robots can better recognize human hand positions, improving coordination, safety, and interaction in collaborative robotic environments.
- **Industry:** Medical and Rehabilitation Research — **Title:** Analyzing Hand Poses for Motor Function Assessment — **Text:** Researchers use this hand images dataset to study hand gestures, fingers alignment, and pose representation in medical diagnostics. It supports pose estimation models that help evaluate motor function recovery, offering an objective tool for rehabilitation analysis and hand tracking in healthcare technology development.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What metadata is available for each participant? — **Answer:** Each sample includes metadata such as age, gender, ethnicity, profession, capture device, dominant hand, and jewelry status.
- **Question:** How was the data collected? — **Answer:** The dataset was collected through a controlled data acquisition process involving 50,000 participants, each providing a complete set of palm images, printed-hand samples, and replay videos. The collection workflow ensured consistent capture protocols while still preserving natural variation in hand appearance, skin tone, and presentation style, resulting in reliable and diverse material for hand-recognition research.
- **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes. Unidata provides free sample images from Open Palm Hand Images Dataset so you can evaluate image quality, labeling accuracy, and camera diversity. This allows you to confirm the dataset’s suitability for your hand detection or gesture recognition model before purchase.
- **Question:** What makes this Unidata dataset different from standard hand image datasets? — **Answer:** Unlike conventional hand datasets, this collection combines genuine palm images, printed-hand attacks, and replay samples within the same dataset. Together with rich demographic metadata and 179,052 annotated images, it supports both hand recognition and biometric anti-spoofing research.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model: free samples are provided for testing and evaluation, while full datasets are available for purchase. This allows organizations to assess dataset relevance before committing to full acquisition.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** Yes. All Unidata datasets are developed in full compliance with GDPR and global data protection laws. The company ensures that all personal and biometric data is lawfully sourced, anonymized, and ethically managed.
- **Question:** How are Unidata datasets stored? — **Answer:** All datasets are securely stored on AWS cloud infrastructure, providing high availability, scalability, and privacy protection. Unidata’s data management adheres to ISO 27001 and ISO 27701 standards, ensuring safe and compliant handling of sensitive biometric information.
- **Question:** How long does it take to receive the dataset? — **Answer:** After submitting a request, Unidata will contact you to confirm dataset details and complete the required agreements. Once payment is processed, the dataset is typically delivered within 3 to 10 business days via secure cloud access.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** This is a real-world dataset featuring genuine human hand images captured by real participants.
- **Question:** Why is jewelry information useful for AI training? — **Answer:** Jewelry such as rings and bracelets can partially occlude the hand or alter its appearance. Including jewelry status in the metadata helps developers build computer vision models that remain accurate under real-world conditions.

## List of Parameters

- **Title:** Tasks — **Description:** Hand Recognition, Computer Vision
- **Title:** Labeling — **Description:** Metadata (Age, gender, ethnicity, profession (or previous job), device, dominant hand, has_jewelry )
- **Title:** Number of files in a set — **Description:** 10 images (6 hand, 2 printed hand, 2 replay)
- **Title:** Number of people — **Description:** 29,842
- **Title:** Data type — **Description:** Image (JPG)

[Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)