#Japan #Tokyo #Qlean Dataset #Visual Bank #Amana Images

Visual Bank's New Launch: Qlean Dataset

Visual Bank Inc., located in Shibuya, Tokyo, led by CEO Masayuki Nagai, has announced a major addition to its Qlean Dataset lineup—an original dataset featuring images of Japanese women with and without makeup. This initiative aims to serve a wide array of research and commercial AI development needs through its innovative data solutions. The addition of this dataset is part of Visual Bank's ongoing effort to broaden its AI development data collection, known as the 'Data Recipe'.

Overview of the Qlean Dataset and Its New Additions

The Qlean Dataset is a carefully curated collection designed for commercial use, featuring original data suitable for various AI applications. The newly added dataset includes photos of 30 Japanese women ranging from their teens to fifties, showcasing various angles, lighting conditions, and both makeup and no-makeup states. This dataset not only provides a versatile resource for developers but also adheres to privacy standards through acquired consent for all subjects featured in the images.

Dataset Specifications:

• - Shooting Environment: Images captured in multiple lighting conditions including bright, dark, downlight, backlight, and via digital cameras.
• - Format: Images are available in both PNG and JPG formats.
• - Image Size: With a total data volume of 6.2GB, it contains 7,124 images at resolutions of 540x960 and 4032x3024 pixels.

Expanding the AI Development Toolbox

The flexibility of the Qlean Dataset allows researchers and developers to customize the dataset to their specific needs. This adaptability comes with the potential for dynamic updates and expansions, aided by partnerships with organizations such as Chiba Lotte Marines and Toyo Keizai, among other domestic and international networks. By minimizing the burdens associated with data collection and organization, these resources accelerate AI development significantly.

Use Cases for the Dataset

The newly introduced dataset serves multiple purposes in AI usability:
1. Face Recognition AI Accuracy Testing: Utilize this dataset as a benchmark to evaluate recognition accuracy regardless of makeup and angle, contributing to the improvement of AI in security and smart lock applications.
2. Automatic Makeup Detection AI Development: Train AI models to distinguish between makeup and no-makeup states based on skin tone and contour features.
3. Impression Change Scoring AI: Quantify impression changes (e.g., elegance, glamour, intelligence) in individuals based on makeup application, optimizing advertising displays and beauty recommendations.
4. Personal Color and Makeup Diagnosis AI: Analyze makeup effects on facial features to recommend color styles based on skin tone and facial characteristics, applicable for beauty salons and e-commerce.
5. Face Type Diagnosis AI: Serve as teacher data for classifying face types (e.g., feminine, cool) to personalize beauty and styling recommendations.
6. Virtual Makeup Try-On AI: Facilitate models that simulate made-up images from raw images for beauty applications and AR services.
7. Skin Care and Impression Change Analysis Models: Evaluate changes in appearance due to aging and makeup effects across a wide age range from 20s to 50s, contributing to age-targeted beauty product recommendations.
8. Biometric Authentication Improvement: Enhance accuracy in biometric verification systems that may vary due to makeup application, thereby reducing erroneous mismatches.
9. AR Avatar and Face Customization AI Learning: Provide a dataset ideal for generating avatar faces and customizations for virtual spaces.
10. Beauty Counseling and Aesthetic Simulations: Quantitative and visual evaluation of appearance differences due to makeup can serve as learning material for AI aimed at beauty and cosmetic consultations.

Why Choose Qlean Dataset?

The Qlean Dataset is structured to maximize return on investment (ROI) while ensuring speed and flexibility in data acquisition. Its unique offering, the Data Recipe, allows for streamlined, customized data procurement that aligns with specific project requirements, making it suitable for one-off unique datasets as well as large-scale commercial applications. The platform also encourages innovations in the AI domain while maintaining compliance with international privacy policies.

Upcoming Events

Visual Bank is also hosting an online seminar on August 20, 2025, focusing on copyright risks associated with generative AI, which could further illuminate legal responsibilities in AI development and implementation. Experts from law firms will be discussing legal frameworks relevant to this field. Participants interested in the legal dynamics surrounding AI research and applications are encouraged to register for this free seminar.

Company Overview

Founded to maximize AI development capabilities through next-generation data infrastructures, Visual Bank operates under the mission of unlocking data potential. They not only provide the Qlean Dataset but also engage in various partnerships aimed at developing IP and AI use cases. Visual Bank has also been recognized in governmental research programs aimed at accelerating social implementations.

• - CEO: Masayuki Nagai
• - Location: WeWork, Nikkei Yotsuya Building, 5-3-23 Kojimachi, Chiyoda-ku, Tokyo, 102-0083
• - Company URL: Visual Bank