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Mobiusi/Face-Recognition-Image-Dataset

Face Recognition Image Dataset Currently, face recognition is widely used in smart devices, but factors such as environmental changes and lighting effects pose challenges to recognition accuracy. Existing datasets often lack diversity and annotation quality, limiting the algorithm's performance improvement. This dataset aims to improve the accuracy of recognition algorithms by providing a rich diversity of high-quality face images. During data collection, various types of camera… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Face-Recognition-Image-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face
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Dataset Card

Face Recognition Image Dataset

Currently, face recognition is widely used in smart devices, but factors such as environmental changes and lighting effects pose challenges to recognition accuracy. Existing datasets often lack diversity and annotation quality, limiting the algorithm's performance improvement. This dataset aims to improve the accuracy of recognition algorithms by providing a rich diversity of high-quality face images. During data collection, various types of camera equipment were used to shoot under different lighting and environmental conditions to ensure data diversity. Multiple rounds of annotation and consistency checks were utilized to ensure annotation accuracy, with the annotation team comprising experienced professionals. Data preprocessing includes steps such as denoising, alignment, and normalization. Data is organized in JPG format for easy storage and access. The core advantage of this dataset is its high-quality data annotation, with annotation accuracy exceeding 98%, and the adoption of new data augmentation techniques such as random cropping and rotation, which improve model robustness. Compared to similar datasets, this dataset holds advantages in diversity and annotation consistency. The provided high-precision data significantly improves the performance of face recognition models in low-light environments, with a 15% increase in recognition rate. Additionally, the dataset covers various skin tones, ages, and genders, offering high rarity and broad generalizability suitable for various face recognition application scenarios.

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
face_countintThe number of faces that can be recognized in the image.
emotion_expressionstringThe primary emotional expression shown on the face, such as smiling or angry.
age_estimationintThe estimated age value based on the face.
gender_predictionstringGender prediction result based on the face, such as male or female.
face_orientationstringInformation about the face's orientation, such as frontal, left profile, or right profile.
image_brightnessfloatThe average brightness value of the image.
skin_tonestringThe primary skin tone type recognized from the face.
accessories_countintThe number of accessories visible on the recognized face, such as glasses or hats.
occlusion_levelfloatThe estimated level of face occlusion, ranging from 0 to 1.

Compliance Statement

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com