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Commercial
Human Ear Detection Biometric Dataset
This is a comprehensive human ear dataset containing annotated images of ears and full-face photos for ear detection, ear classification, and biometric recognition tasks, supporting human identification, forensic comparison, and model training using diverse ear shapes, lighting conditions, and recognition systems in large-scale biometric datasets.
-
- images
- 14,000+
-
- people
- 2,000
- Computer Vision
- Machine learning
- Medical
- Classification
- Forensics
- Computer Vision
- Machine learning
- Medical
- Classification
- Forensics
This is a comprehensive human ear dataset containing annotated images of ears and full-face photos for ear detection, ear classification, and biometric recognition tasks, supporting human identification, forensic comparison, and model training using diverse ear shapes, lighting conditions, and recognition systems in large-scale biometric datasets.
- Computer Vision
- Machine learning
- Medical
- Classification
- Forensics
-
- images
- 14,000+
-
- people
- 2,000
Dataset Info
| Characteristic | Data |
| Description | Images of an ear with a face photo |
| Data types | Image |
| Tasks | Ear‑biometric R&D, Forensic comparison, and missing‑person identification |
| Number of images | 14,000 |
| Number of people | 2,000 |
| Number of files in a set | 7 (Full face - 1, Left ear - 3, Right ear - 3) |
| Labeling | Metadata (age, gender, ethnicity, side, proximity, lighting) |
| Gender | Male, Female |
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Technical
Characteristics
| Characteristic | Data |
| Image extensions | JPG, JPEG |
| Devices | Canon R6, iPhone 15, etc |
Dataset Use Cases
FAQs
What is this dataset used for?
Human Ear Detection Biometric Dataset is primarily used for ear recognition research, forensic comparison, and person identification tasks. It supports the development of biometric systems, deep learning models, and computer vision algorithms that rely on ear shape and facial features for accurate identity verification.
What types of annotations are provided?
The dataset provides detailed metadata annotations, including age, gender, ethnicity, ear side, lighting, and camera proximity. These annotations are crucial for training deep neural networks and performing biometric analysis and classification in ear-based identification systems.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. You can request a free sample of Human Ear Detection Biometric Dataset to evaluate image quality, diversity of ear shapes, and metadata accuracy. The sample helps determine whether the dataset fits your machine learning, biometric research, or ear recognition objectives before buying.
Is it possible to request a custom dataset?
Yes. Unidata provides custom dataset services that allow clients to specify age groups, ethnic distribution, lighting conditions, or image resolutions. Custom datasets are ideal for organizations developing specialized biometric models or forensic analysis tools requiring specific ear detection or classification parameters.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free samples are available for testing and validation, while complete datasets are accessible exclusively through purchase, providing flexibility for academic and commercial research teams.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All datasets, including Human Ear Detection Biometric Dataset, comply with GDPR and other international data protection regulations. Every image is collected from legally permissible sources, ensuring ethical, privacy-safe, and lawful data usage in biometric research.
How are Unidata datasets stored?
Unidata securely stores all datasets on AWS cloud infrastructure, ensuring data integrity, scalability, and high availability. The storage and management process complies with ISO 27001 and ISO 27701 standards, offering a secure and privacy-focused environment for handling biometric image data.
Why does the dataset include both ear images and full-face photos?
The combination of ear images and corresponding full-face photos enables AI models to learn relationships between facial features and ear biometrics. This supports more accurate ear detection, biometric recognition, forensic comparison, and identity verification in real-world scenarios.
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