Dataset

65000 Images Face Landmark Dataset-57 Key Points

Global Diverse Facial Dataset Library for Face Recognition

Introducing our high-quality facial dataset consisting of 65,000 images, designed specifically for advanced face recognition systems. This collection encompasses a diverse range of ethnic groups including Chinese, African, Central Asian, Caucasian, and Indian individuals. Each image in our face dataset is meticulously annotated with 57 keypoints, offering you a comprehensive and detailed 57-keypoint annotation database.

Highlighted Features

Diverse Ethnic Coverage:

Our facial dataset ensures superior performance of your face recognition technology across various ethnicities.

High-Precision Keypoint Annotation:

Each face is precisely annotated with 57 keypoints, providing accurate training data for your algorithms.

Broad Application Scenarios:

Whether you're developing face recognition software, emotion analysis tools, or other facial-related research, our dataset caters to your needs.

Invest in our face dataset and elevate the capabilities of your facial recognition technology!

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Data Name	65000 Images Face Landmark Dataset-57 Key Points
Producer	Surfingtech
IPR Ownership	Surfingtech
Quantity	4200 ID
Ethnicity	Chinese people: 850，African people: 1300，Central Asian people: 850 Caucasian people: 750，Indian people: 450
Details (each person)	65000 images. there is one face in one image. we mark the 57 keypoinst. In this dataset, there are 1300 african, 850 asian, 850 central asian, 750 caucasian and 450 indian.

Our dataset boasts a wide array of multiracial facial images and videos, captured under different lighting conditions

It encompasses multiple expressions, postures, and facial occlusion data.

Furthermore, we ensure the quality of our data by providing high-resolution imagery, even under different lighting conditions.

Diversity is at the core of our dataset. It features individuals from various age groups and maintains a balanced gender ratio. More importantly, our dataset is globally inclusive, capturing the unique facial features of various ethnicities, including Africans, Asians, Caucasians, and Southeast Asians, ensuring a comprehensive representation for your AI applications.

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