Gesture Recognition Image Dataset
#image classification #computer vision #gesture recognition #machine learning #smartphones #wearable devices #autonomous driving #human-computer interaction #virtual reality
- 500 records
- 1.2G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
With the proliferation of smart devices, gesture recognition has become an important means to enhance user interaction experience. However, existing gesture recognition technologies face challenges like low accuracy in complex backgrounds, difficulty in dynamic gesture recognition, and poor cross-device recognition consistency. Current solutions often rely on relatively fixed scenarios and single data sources, making it difficult to meet the diverse application needs. This dataset aims to solve the generalization and adaptability issues of gesture recognition algorithms by providing high-quality image data covering a wide variety of gestures and application scenarios.The dataset collects images of various gesture poses using high-resolution cameras under different lighting conditions and backgrounds. Quality control measures include multiple rounds of labeling, consistency checks, and expert reviews to ensure data accuracy and consistency. The labeling team consists of experts in the field of computer vision and experienced labeling staff, with a scale of more than 50 people. The data preprocessing processes include image normalization, data augmentation, and denoising to ensure efficient model training. Data is stored in JPG format and organized into a category-based file structure for easy access and retrieval.The dataset is characterized by high labeling precision and good consistency, with labeling precision reaching over 98%, covering a broad range of gesture types to ensure completeness. Technical innovations include multimodal data fusion and new methods for dynamic gesture labeling, enhancing the robustness and accuracy of recognition algorithms in complex scenarios. At the same time, the dataset aids in developing stronger cross-device gesture recognition systems, improving performance metrics by 15%. Compared to existing datasets, this dataset provides more diversified gestures and application scenarios, offering significant scarcity and expansiveness and can be widely applied to various smart devices.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| gesture_type | string | The specific type of gesture present in the image. |
| hand_position | string | Description of the hand's position in the image, e.g., 'top left' or 'center'. |
| background_complexity | integer | The complexity level of the image background, typically indicated on a scale from 1 to 5. |
| lighting_condition | string | The lighting condition in the image, such as 'bright', 'normal', or 'dim'. |
| skin_tone | string | The skin tone of the hand in the image, such as 'light', 'medium', or 'dark'. |
| image_quality | float | The image quality rating, typically between 0 and 1, with 1 being the best. |
| occlusion_level | integer | The level of occlusion of the hand in the image, typically indicated on a scale from 1 to 5. |
| multiple_hands | boolean | Indicates whether multiple hands are present in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- In which smart devices can the gesture recognition image dataset be applied?
- The gesture recognition image dataset can be applied in various smart devices such as smartphones, tablets, smart home devices, and wearable devices.
- What features make this dataset suitable for gesture recognition?
- This dataset contains a large number of diverse gesture images, which helps train high-precision gesture recognition models, thereby enhancing the performance of smart devices.
- What are the benefits of using this dataset for gesture recognition?
- Using this dataset can improve the accuracy and reliability of gesture recognition models, thereby enhancing user experience and the convenience of device interaction.
- Does the gesture recognition image dataset support recognition in different environments?
- Yes, the dataset includes gesture images taken under various lighting and background conditions, supporting recognition in different environments.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Gesture Recognition Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/2cc2d108d467b0d76959435436331736},
urldate={2026-02-04},
keywords={gesture recognition, image classification, smart device dataset, computer vision data, machine learning image data},
version={1.0}
}Using this in research? Please cite us.
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