Image / Education
Open Palm Hand Images Dataset - 500,000 Images for Hand Detection and Gesture Recognition
Advertised by providerLicensing rights not independently verifiedPaid licence
Overview
Open Palm Hand Images Dataset - 500,000 Images for Hand Detection and Gesture Recognition is an image dataset. The provider states a volume of 500,000 images. Typical applications include object detection. Relevant sectors: Education. The listing mentions keypoints and classification labels. It is offered as a paid dataset; pricing details are provided on request.
Primary applications
What the listing offers
- Stated volume of 500k images.
- Listing mentions keypoints, classification labels.
Overview The Open Palm Hand Images Dataset is a large-scale commercial biometric dataset produced by the provider, designed to support hand detection, palm recognition, and gesture analysis research. It includes 500,000 annotated images collected from 50,000 participants, making it one of the largest datasets of its kind for computer vision model training. Each subject contributes a standardized set of 10 files: - 6 open-palm photos (live hand images) - 2 printed-hand images - 2 replay videos The dataset covers both right hands and left hands in various configurations, enabling comprehensive research into hand tracking, pose estimation, and gesture recognition across a wide range of real-world conditions. Subject Demographics The dataset represents broad demographic diversity across 50,000 participants. Both male and female subjects are included. Each record is labeled with the following metadata attributes: 1. Age 2. Gender 3. Ethnicity 4. Profession (or previous job) 5. Device type used for capture 6. Dominant hand 7. Jewelry presence (has_jewelry) This level of granularity makes the dataset well-suited for building unbiased computer vision models that generalize across diverse human populations. Data Collection & Technical Specifications Data was collected via crowdsourcing platforms under consistent capture protocols, ensuring natural variation in hand appearance, skin tone, and presentation style while maintaining reliable image quality. 1. File format: JPG 2. Labeling type:
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