#Japan #Tokyo #Qlean Dataset #Visual Bank #AI Data Solution

Visual Bank's New Qlean Dataset: A Comprehensive AI Resource

Visual Bank, a prominent startup based in Shibuya, Tokyo, has recently unveiled the Qlean Dataset, which is designed to aid AI development for research and commercial uses. This dataset is a staple of their innovative AI learning data solution, which is continuously expanding to meet the needs of developers and researchers alike. With the introduction of the "multinational facial image dataset with and without accessories," Visual Bank is enhancing its offerings in the AI landscape. This dataset is part of what they call their "Data Recipe" lineup, which allows for a flexible combination of ready-to-use data materials tailored to various applications.

Understanding the Data Recipe Concept

The term "Data Recipe" refers to a unique and commercially viable array of original data available through the Qlean Dataset. It is characterized by its adaptability, allowing users to efficiently mix and match data according to their specific requirements, precision needs, and deadlines. Visual Bank provides both partially annotated and unannotated data, adjusting the contents based on custom criteria. Moreover, strategic partnerships with organizations like the Chiba Lotte Marines and Toyo Keizai Inc., coupled with an extensive domestic and international network, support the ongoing expansion of the dataset.

This means that the burden of data collection and preparation for AI projects can be significantly reduced, leading to accelerated development processes in the industry.

You can find more details about the Qlean Dataset here.

An Overview of the New Multinational Face Image Dataset

The latest addition to Visual Bank's dataset lineup includes:

• - Data Type: High-quality facial images featuring 110 models captured under various conditions, including the presence or absence of accessories like masks and glasses, across different lighting conditions.
• - Diversity: This collection encompasses individuals from various ethnic backgrounds including Black, White, and South Asian individuals, providing a rich resource for testing and improving AI models.
• - Technical Specifications:

- Total Models: 110
- Data Format: PNG/JPEG
- Number of Files: 15,232
- Total Size: 12.7 GB
- Pixel Dimensions: 540 x 960 (some at 4032 x 3024)
- Shooting Equipment: IP cameras and digital cameras
- Capture Distance: Close enough to include the entire face or upper body
- Height: Captured at a height close to the face level

Use Case Scenarios

1. Enhancing Diversity in Facial Recognition AI: By incorporating images of individuals from different ethnic backgrounds and age groups, the dataset is optimal for validating and correcting biases related to race, age, and accessory presence in AI models.

2. Improving Identity Verification Accuracy: Including images of individuals wearing masks makes this dataset particularly useful for identity verification systems in environments where masks are commonly worn, such as airports and hospitals.

3. Training Models for AR/VR: The dataset includes facial images from various angles and resolutions, making it ideal for improving the algorithms used for detecting facial expressions and accessories during the use of headsets in augmented or virtual reality applications.

4. Gesture Recognition and Eye-Tracking AI: The diverse angles captured, including front, side, and overhead views, cater to the requirements of developing eye-tracking and face orientation estimation models, essential for driver monitoring and human-computer interaction systems.

5. Benchmarking for Surveillance AI: Real-world images captured through IP cameras and digital cameras serve as excellent benchmarks for evaluating face identification models used in security cameras.

6. AI-Driven Avatar Generation: With a variety of ages, races, and accessories among the facial features, this dataset is invaluable for training algorithms that generate realistic 3D avatars reflective of users' uniqueness.

Key Features of the Qlean Dataset

• - Fully Compliant for Research and Commercial Use: All subjects in the Qlean Dataset have provided consent for data collection and use in research and commercial applications, ensuring compliance with privacy policies across different countries.
• - Speedy and ROI-Focused Data Acquisition: The unique Data Recipe model allows for more cost-efficient initial data procurement, maximizing ROI for users.
• - Customization Options: If specific datasets are needed that do not exist in the Data Recipe lineup, Visual Bank can create custom datasets that suit unique specifications.

For further inquiries regarding the Qlean Dataset, please refer to the contact page. To explore the Qlean Dataset services, visit this link.

About Visual Bank Inc.

Visual Bank is dedicated to building next-generation data infrastructure aimed at maximizing AI development potential. Their mission is to unleash the possible applications of all data. With a subsidiary focused on AI learning datasets and a commitment to developing IP and AI use cases through projects like "THE PEN," Visual Bank is at the forefront of the AI industry in Japan.

CEO: Masayuki Nagai
Location: WeWork, Nippon Television Yotsuya Building, 5-3-23 Kojimachi, Chiyoda-ku, Tokyo 102-0083
Visit Visual Bank's website here and learn more about Amana Images here.