---
title: "Facial Skin Condition Dataset"
description: "High-resolution skin condition face dataset containing annotated images of human faces with visible skin problems such as acne, redness, and eye bags, designed to support…"
url: "https://unidata.pro/datasets/facial-skin-condition-image-dataset/"
date_modified: "2026-05-27T11:24:05+03:00"
language: "en-US"
---
High-resolution skin condition face dataset containing annotated images of human faces with visible skin problems such as acne, redness, and eye bags, designed to support skin condition detection, disease classification, and training of computer vision and learning models across diverse skin types and dermatology conditions.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 639 — **Text:** images
- **Number:** 213 — **Text:** people

### Tooltips Section

**Tooltip items:**

- **Name:** Medicine
- **Name:** Computer Vision
- **Name:** Machine Learning
- **Name:** Facial Recognition

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Images of human face with visible skin texture/issues |
| Data types | Image |
| Tasks | Skin Condition Detection, Computer Vision |
| Number of people | 213 |
| Number of files in a set | 3 images per person |
| Total number of files | 639 |
| Labeling | Metadata (ID, gender, age, ethnicity) |
| Gender | Male, Female |
| Types of skin problems | 4 (acne,acne black, bags, redness) |
| Types of face angles | Frontal, left, right |

**Media Slider:**

- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/facial-skin-condition-image-dataset0a-2-scaled.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/facial-skin-condition-image-dataset0a-example.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/facial-skin-condition-image-dataset0a-example-2.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1QQp4PL3qn0BLttIVK6D0QstLtNpZVjfo?usp=sharing)

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id="17"] — **caption above the graph:** Gender Distribution
- **Shortcode:** [ays_chart id='19'] — **caption above the graph:** Distribution by Type of Skin Problem
- **Shortcode:** [ays_chart id="18"] — **caption above the graph:** Age Distribution

### 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.

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Healthcare & Dermatology — **Title:** AI for Early Skin Condition Detection — **Text:** Facial Skin Condition Dataset supports dermatology research and AI model training for detecting various skin diseases. Containing high-quality facial images with different skin types and tones, it enables deep learning algorithms to identify lesions, acne, and pigmentation issues, improving early diagnosis and personalized treatment recommendations.
- **Industry:** Cosmetic & Skincare Industry — **Title:** Personalized Skin Analysis Systems — **Text:** This skin condition dataset helps cosmetic brands develop AI-powered skincare tools. By analyzing diverse facial features and skin tones, companies can develop accurate diagnostic systems to identify dryness, sensitivity, or acne. It enhances product recommendations and the customer experience by providing data-driven insights into real skin conditions.
- **Industry:** Medical AI Research — **Title:** Training Models for Dermatology Applications — **Text:** Researchers use this human faces dataset to train deep neural networks for precise skin condition classification. With diverse dermatological images and labeled data, it supports disease detection across multiple categories such as rosacea, eczema, and melasma, helping build models that improve diagnostic accuracy across global populations.
- **Industry:** Telemedicine & Digital Health — **Title:** Remote Skin Condition Assessment — **Text:** Telemedicine platforms benefit from datasets containing facial skin problems for remote diagnosis. Using this dataset, AI systems can assess dermatological conditions via images captured on mobile devices. It enables faster triage, remote consultations, and early detection of potential skin diseases, making healthcare more accessible and efficient.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What types of annotations are provided? — **Answer:** Each image includes detailed metadata labels specifying the individual’s demographic attributes (age, gender, ethnicity) and visible skin issues such as acne, blackheads, redness, or eye bags.
- **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes. Unidata offers free sample images from Facial Skin Condition Dataset to help users evaluate data quality, diversity of skin tones, and labeling accuracy. These samples allow you to confirm that the dataset aligns with your skin condition detection or dermatology research needs.
- **Question:** Which facial skin conditions are represented in the dataset? — **Answer:** The dataset includes images designed for training AI systems to recognize visible facial skin conditions such as acne, pigmentation changes, redness, wrinkles, pores, and other dermatological characteristics, depending on the available annotations.
- **Question:** What are the sources of data for Unidata datasets? — **Answer:** All images in Facial Skin Condition Dataset were collected via verified crowdsourcing platforms.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model. Free samples are available for evaluation and testing, while full datasets are offered exclusively through purchase for commercial or academic use. This ensures flexibility for research and enterprise users alike.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** Yes. All datasets, including Facial Skin Condition Dataset, are curated in full compliance with GDPR and applicable data protection laws. Data is sourced ethically and lawfully, ensuring anonymized, non-identifiable imagery and strict privacy safeguards.
- **Question:** How are Unidata datasets stored? — **Answer:** Unidata securely stores all datasets on AWS cloud infrastructure, offering high reliability, availability, and compliance with ISO 27001 and ISO 27701 standards. This ensures that facial images and metadata are handled in a secure, privacy-conscious, and globally compliant environment.
- **Question:** How long does it take to receive the dataset? — **Answer:** Once your request is submitted, Unidata will contact you to confirm dataset details and complete the necessary documentation. After the purchase agreement is finalized, the dataset will be delivered securely within 3–10 business days.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** This is a real-world dataset featuring authentic human faces captured under real conditions.

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