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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.

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  • images
    14,000+
  • people
    2,000
  • 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.

Get in touch Download sample
  • 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
Source and collection methodology; Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Biometric Security and Identification

    Enhancing Human Recognition through Ear Biometrics

    Human Ear Detection Biometric Dataset provides a comprehensive collection of high-resolution human ear images to develop and train biometric identification systems. The dataset supports ear recognition across diverse ear shapes, lighting conditions, and angles, enabling accurate identification and improving recognition rates in secure access, identity verification, and biometric security applications.

  • Computer Vision and Object Detection

    Developing Reliable Ear Detection Models

    This ear detection dataset supplies annotated human ears for training object detection algorithms and deep learning models. Researchers and engineers can use the training data to enhance detection performance, refine feature extraction techniques, and build robust recognition systems capable of locating ears in real-world conditions such as partial occlusions, varying head poses, and different environments.

  • Biometric Research and Algorithm Benchmarking

    Evaluating Recognition Accuracy in Complex Conditions

    The human ear dataset enables benchmarking of ear classification systems and learning algorithms for biometric identification. Researchers can test recognition accuracy under challenging conditions, including diverse ear shapes, orientations, and lighting variations. The dataset facilitates the evaluation of detection techniques and the performance of neural networks in controlled and real-world scenarios.

  • Multimodal Biometric Systems

    Integrating Ear and Face Recognition for Improved Accuracy

    By combining ear detection with facial recognition, this ear classification dataset supports multimodal biometric systems that enhance human identification accuracy. The dataset provides high-quality training data for deep neural networks, allowing models to leverage multiple biometric features simultaneously, improving recognition rates, reducing errors, and strengthening security in access control and identity verification applications.

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 is included in this dataset?
This dataset includes 14,000 high-resolution images of 2,000 individuals, featuring both left and right ear images along with corresponding full-face photos. Each set contains seven images - one face, three left ears, and three right ears - captured under varied lighting and proximity conditions.
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.
Still have questions about using Unidata datasets? Read our user-guides

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