---
title: "Kids & Teens Aging Dataset (Ages 7-21)"
description: "The dataset was created for age estimation, age progression analysis, and facial aging studies in children and teenagers aged 7–21. This face image dataset includes…"
url: "https://unidata.pro/datasets/kids-teens-aging-7-21/"
date_modified: "2026-10-23T13:18:56+03:00"
language: "en-US"
---
The dataset was created for age estimation, age progression analysis, and facial aging studies in children and teenagers aged 7–21. This face image dataset includes photos annotated with the Monk Skin Tone (MST) scale, camera angle, whether the shot is a digital or physical (scanned) photo, accessories worn, blur, and background handling, alongside demographic metadata (gender, ethnicity, skin tone category).

## List of Datasets

- [Kids & Teens Aging Dataset (Ages 7-15)](https://unidata.pro/datasets/kids-teens-aging/)
- [iBeta Kids Dataset](https://unidata.pro/datasets/ibeta-kids/)
- [Selfie with ID Dataset](https://unidata.pro/datasets/selfie-with-id/)
- [iBeta Level 1 Dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/)

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 8,039 — **Text:** Photos
- **Number:** 536 — **Text:** People

### Tooltips Section

**Tooltip items:**

- **Name:** Facial Recognition
- **Name:** Age Estimation
- **Name:** Re-identification
- **Name:** Security
- **Name:** Computer Vision

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Photos of children and teenagers for age progression analysis. Each photo is annotated with the Monk Skin Tone (MST) scale |
| Data types | Image |
| Tasks | Face recognition, Computer Vision, Biometric Verification |
| Total number of images | 8,039 |
| Total number of people | 536 |
| Number of files in a set | 14-15 |
| Labeling | Only technical characteristics and metadata (age, age_months, gender, ethnicity, skin, mst, camera_type, is_digital, accessories, blur, background_processing, people_on_the_background) |
| Gender | Female, Male |
| Ethnicity | Caucasian, Asian, African, Hispanic, South Asian, Middle Eastern |
| Skin tone category | Light, Medium, Brown, Dark |
| Age | 7-21 |

**Media Slider:**

- **Image in the slider:** ![Kids & Teens Aging Dataset (Ages 7-21)](https://unidata.pro/wp-content/uploads/2026/10/kids-teens-7-21.webp)
- **Image in the slider:** ![Kids & Teens Aging Dataset (Ages 7-21)](https://unidata.pro/wp-content/uploads/2026/10/kids-teens-7-21-2-scaled.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/14KsjVJ_2bl0iYmXdzy347yrq-qHQDw9C)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image extensions | JPG, PNG, webp 4 |
| Camera type | Back, Front |

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

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id='88'] — **caption above the graph:** Distribution by gender
- **Shortcode:** [ays_chart id='89'] — **caption above the graph:** Distribution by ethnicity
- **Shortcode:** [ays_chart id='90'] — **caption above the graph:** Distribution by skin tone
- **Shortcode:** [ays_chart id='92'] — **caption above the graph:** Distribution by Monk Skin Tone
- **Shortcode:** [ays_chart id='93'] — **caption above the graph:** Distribution by accessories (by photo, top values)
- **Shortcode:** [ays_chart id="91"] — **caption above the graph:** Distribution by camera type
- **Shortcode:** [ays_chart id='94'] — **caption above the graph:** Distribution by background processing (by photo)

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Computer Vision — **Title:** Facial Age Estimation — **Text:** The dataset supports deep learning models that estimate apparent age from facial images. With participants aged 7–21 and detailed age labels, researchers can study age-related facial changes, compare estimation methods, and evaluate model performance across younger age groups and diverse skin tones.
- **Industry:** Biometric Technology — **Title:** Face Recognition and Verification — **Text:** Facial recognition developers can use this faces dataset to examine how facial features change during childhood and adolescence. Age-related annotations and demographic metadata help researchers assess recognition accuracy across different ages, supporting the development and testing of biometric verification systems designed to handle changing facial appearance over time.
- **Industry:** Healthcare Research — **Title:** Facial Development Analysis — **Text:** Researchers can examine age-related facial characteristics to study visible changes during childhood and adolescence. The dataset’s age annotations, gender information, ethnicity labels, and Monk Skin Tone classifications provide demographic context for facial image analysis. These attributes may support exploratory research into apparent age and patterns of facial development.
- **Industry:** Dataset Benchmarking — **Title:** Model Performance Evaluation — **Text:** This age estimation dataset provides labeled facial images for testing and comparing machine learning methods. Its age and demographic metadata support evaluation across participant groups, helping researchers identify estimation errors, assess model consistency, and investigate how image quality, camera type, and background conditions affect age estimation results.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** Can I request a sample of the Kids & Teens Aging Dataset before purchasing? — **Answer:** Yes, you can request a sample of the Kids & Teens Aging Dataset before purchasing the full collection. Free samples are available for trial and testing, allowing you to assess image quality, age labels, metadata structure, and suitability for your age estimation models.
- **Question:** What types of annotations are provided in this age estimation dataset? — **Answer:** The dataset provides technical metadata and demographic annotations, including age, age in months, gender, ethnicity, skin category, Monk Skin Tone (MST), camera type, and image characteristics. Additional fields include accessories, blur, background processing, whether people appear in the background, and whether an image is digital.
- **Question:** How many images and participants are included in the dataset? — **Answer:** The dataset contains 8,039 images from 536 individuals, with approximately 14–15 files per set.
- **Question:** What is the Monk Skin Tone scale, and how is it used in this dataset? — **Answer:** The Monk Skin Tone (MST) scale is a framework for categorizing skin tones, and the dataset description specifies that each photo is annotated using this scale. These annotations can support research into model performance across skin tones and help identify potential disparities in facial analysis and age estimation.
- **Question:** Which ethnic groups are represented in kids & teens aging dataset? — **Answer:** The dataset’s ethnicity metadata includes Caucasian, Asian, African, Hispanic, South Asian, and Middle Eastern categories.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model: free samples are provided for trial and testing, while complete datasets are available exclusively through purchase. This approach allows users to assess a dataset’s suitability before purchasing the full collection.
- **Question:** How are Unidata datasets stored? — **Answer:** Unidata stores all datasets securely on AWS cloud infrastructure, supporting high availability and scalability. Its storage and management practices are aligned with ISO 27001 and ISO 27701 standards for information security and privacy management.
- **Question:** How long does it take to receive the dataset? — **Answer:** After you submit a request, Unidata will contact you to review the requirements and complete the necessary documentation. Following signing and payment, the dataset will be delivered within 3–10 days.

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