Facial Color Segmentation Dataset
Project Overview:
Objective
To curate a dataset focusing on the segmentation of different color zones on human faces, the aim is to boost advancements in AI models for makeup simulation apps, dermatological studies, and digital artistry. This dataset will facilitate the development of robust algorithms and models. Moreover, it will provide valuable insights into the diverse color distribution across various facial regions.
Scope
Amassing images of diverse human faces under varied lighting conditions, we will annotate them to segment the face into different color zones: forehead, cheeks, chin, nose, and the areas under the eyes. Additionally, we’ll ensure consistency across the dataset to maintain accuracy in our analysis. Furthermore, by incorporating a wide range of lighting conditions, we aim to capture the nuances of facial features under different environmental circumstances. Moreover, this diverse dataset will enable us to develop robust algorithms that can accurately identify and classify facial regions regardless of lighting variations.
Sources
- Collaborations forged with skincare clinics and makeup artists have resulted in a carefully collected and successfully curated repository of beauty-related content. As a result, the repository now boasts an array of informative articles, captivating videos, and engaging social media posts. Furthermore, these collaborations have allowed for the creation of comprehensive beauty guides, insightful tutorials, and exclusive behind-the-scenes peeks into the world of skincare and makeup. Additionally, the partnerships have facilitated the exchange of knowledge, expertise, and innovative techniques among professionals in the beauty industry.
- Engaged diverse volunteers from various ethnicities, age groups, and genders, thereby contributing to a thoughtfully collected and well-curated pool of participants.
- I meticulously utilized image databases with the necessary permissions. Additionally, I ensured an ethically curated assortment of visual resources.
- Conducted sponsored public photo campaigns, consequently resulting in a successfully collected and thoughtfully curated collection of promotional materials.
Data Collection Metrics
- Total Face Images: 400,000
- Natural Lighting: 150,000
- Artificial Lighting: 100,000
- Mixed Lighting: 50,000
- Special Conditions (e.g., flash, colored lighting): 100,000
Annotation Process
Stages
- Image Pre-processing: Refinement for clarity, contrast, and standardization.
- Facial Zone Segmentation: Delicately demarcating areas like the forehead, cheeks, chin, nose, and under-eyes using segmentation masks.
- Metadata Annotation: Details regarding skin type (oily, dry, combination, etc.), apparent skin health, and the presence of makeup or facial treatments.
- Validation: Cross-referencing annotations using both dermatologists and preliminary color segmentation models.
Annotation Metrics
- Total Facial Zone Segmentation Annotations: 2,000,000 (5 zones per image)
- Metadata Annotations: 400,000
Quality Assurance
Stages
Automated Color Zone Recognition Verification:Â Initial models validate segmented facial zones.
Peer Review:Â A fresh set of annotators assesses a subset of images for double validation.
Inter-annotator Agreement:Â Some images are marked by multiple annotators to guarantee consistency in zone determination.
QA Metrics
- Annotations Validated using Color Zone Recognition: 200,000 (50% of total images)
- Peer Reviewed Annotations: 120,000 (30% of total images)
- Inconsistencies Identified and Corrected: 8,000 (2% of total images)
Conclusion
The Facial Color Segmentation Dataset serves as a crucial asset for ventures within the skincare, cosmetics, and digital enhancement domains. Furthermore, with its keen focus on the intricate nuances of facial color zones, it sets the stage for innovative applications in skin health analysis, virtual makeup testing, and digital art creation. Additionally, harnessing this dataset ensures that technologies can adeptly recognize and interpret the multifaceted color patterns on human faces.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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