---
title: "Kids Anti-Spoofing Dataset"
description: "The dataset provides 6,000 high-quality facial images of children aged 7–15 for face anti-spoofing and liveness detection tasks. This child safety dataset supports research in…"
url: "https://unidata.pro/datasets/kids-anti-spoofing/"
date_modified: "2026-03-23T16:09:07+03:00"
language: "en-US"
---
The dataset provides 6,000 high-quality facial images of children aged 7–15 for face anti-spoofing and liveness detection tasks. This child safety dataset supports research in biometric systems, helping improve facial recognition accuracy, detect spoofing attacks, and build safer AI models for protecting kids in digital and identification environments.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 6 000 — **Text:** Images
- **Number:** 300 — **Text:** people

### Tooltips Section

**Tooltip items:**

- **Name:** Facial Recognition
- **Name:** Security
- **Name:** Anti-spoofing
- **Name:** Computer Vision
- **Name:** Machine Learning

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Photos of children and teenagers for anti-spoofing tasks. |
| Data types | Image |
| Tasks | Face recognition, Computer Vision |
| Total number of files | 6000 |
| Number of files in a set | 300 |
| Labeling | Metadata (ID, gender, age, ethnicity) |
| Age | 7-15 |
| Gender | Male, Female |

**Media Slider:**

- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-1.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-2.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-3.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-4.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-5.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-6.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-7.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-8.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-9.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-10.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-11.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-12.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-13.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-14.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-15.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-16.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-17.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-18.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-19.webp)
- **Image in the slider:** ![Kids Anti-Spoofing Dataset](https://unidata.pro/wp-content/uploads/2025/11/kids-anti-spoof-20.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1oOLYX68wwOyhXr0hyqTQJd2AN20ocQCk?usp=sharing)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image Extension | JPG |

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

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id='20'] — **caption above the graph:** Gender Distribution
- **Shortcode:** [ays_chart id="21"] — **caption above the graph:** Age Distribution

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Education Technology — **Title:** Protecting Students in Digital Classrooms — **Text:** Kids Antispoofing Dataset helps developers create safe authentication systems for online learning. By recognizing real student faces and filtering spoofing attempts, it prevents impersonation in virtual classes. Schools and EdTech providers use it to ensure verified participation, protect minors’ privacy, and build trusted digital education environments.
- **Industry:** Consumer Electronics — **Title:** Securing Smart Devices for Families — **Text:** Manufacturers of tablets, smart toys, and cameras can use Kids Antispoofing Dataset to train facial recognition systems that can differentiate real child users from spoofed inputs. This helps design devices with built-in liveness detection, ensuring parental controls and privacy settings function safely and accurately.
- **Industry:** Biometric Security — **Title:** Building Reliable Child Safety Systems — **Text:** Security firms use the Kids Antispoofing Dataset to design facial recognition tools that protect children from identity fraud and spoofing attacks. It includes real and fake samples—print, 2D mask, 3D mask, and replay—helping train systems to detect deceptive inputs and ensure secure access in public and private spaces.
- **Industry:** Child Protection Technology — **Title:** Enhancing Safety in Digital Platforms — **Text:** Developers of child safety tools use this dataset to improve recognition systems that verify children’s identities during online interactions. Supporting accurate detection of spoofing attacks helps platforms prevent fake profiles, ensure safe digital access, and create environments where children can learn, play, and communicate securely.

### Fact

**FAQs Heading:** FAQ

**List of Questions:**

- **Question:** Is the dataset compatible with common AI frameworks? — **Answer:** Yes. All images are provided in the widely supported JPG format, allowing easy integration with machine learning libraries such as TensorFlow, PyTorch, OpenCV, and other computer vision frameworks.
- **Question:** What age range does the dataset cover? — **Answer:** The dataset includes images of children and teenagers between 7 and 15 years old. This age range supports the development and evaluation of biometric systems intended for schools, child identity verification, parental consent platforms, and youth-focused digital services.
- **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes, Unidata provides a free dataset sample for review. It includes a small portion of children’s face images with full metadata annotations, allowing you to evaluate the dataset’s image quality, labeling format, and anti-spoofing suitability before making a purchase.
- **Question:** What are the sources of data for Unidata datasets? — **Answer:** Unidata datasets are collected through controlled crowdsourcing platforms to ensure authenticity, consent, and diversity. Kids Anti-Spoofing Dataset includes ethically sourced facial images of children and teenagers, gathered under verified conditions that comply with privacy protection and data collection regulations.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model. Free samples are offered for testing and evaluation, while the complete datasets are available through purchase-only access for professional or commercial research applications.
- **Question:** Do Unidata datasets comply with GDPR or other data privacy regulations?` — **Answer:** Yes. All Unidata datasets comply fully with GDPR and relevant data protection laws. Data is collected through lawful and transparent means, and no personally identifiable information is disclosed, ensuring ethical AI dataset development.
- **Question:** How are Unidata datasets stored? — **Answer:** All datasets are securely stored on AWS cloud infrastructure, ensuring data reliability and availability. Unidata’s practices comply with ISO 27001 and ISO 27701 standards, guaranteeing robust information security and privacy management for sensitive data.
- **Question:** How long does it take to receive the dataset? — **Answer:** After submitting a request, Unidata will confirm your requirements and send the necessary documentation. Once the payment process is complete, the dataset is securely delivered within 3–10 business days through encrypted cloud access.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** This is a real-world dataset, containing genuine images of children and teenagers captured in controlled settings.

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