Lips Segmentation Dataset

Project Overview:

Objective

Develop a comprehensive dataset focused on the precise segmentation of lips in various contexts. This dataset will support advancements in facial recognition, virtual makeup applications, medical diagnostics related to oral health, and emotion-based AI analyses.

Scope

Collate a vast range of images highlighting lips in different lighting, emotions, orientations, and lipstick applications. Each image will be finely annotated to delineate the boundaries of the lips.

Lips Segmentation Dataset
Lips Segmentation Dataset
Lips Segmentation Dataset
Lips Segmentation Dataset

Sources

  • Engaged in collaborative initiatives with dermatologists and cosmetic surgeons, resulting in a meticulously collected and successfully curated range of resources.
  • Crowdsourced inputs from global participants have been carefully collected to ensure diversity in lip shapes, sizes, and colors in the dataset.
  • Collaborated with the movie and entertainment industries for capturing different emotional expressions, contributing to a successfully collected and thoughtfully curated set of visuals.
  • Partnered with makeup artists and beauty schools to gather a carefully collected and successfully curated assortment of beauty-related content.
Lips Segmentation Dataset
Lips Segmentation Dataset

Data Collection Metrics

  • Total Images: 25,000
  • Neutral Expression: 10,000
  • Varied Emotions: 8,000
  • Lipstick Applications: 5,000
  • Medical Cases: 2,000

Annotation Process

Stages

  1. Image Pre-processing: Adjustment for clarity, contrast, and color to assure uniform quality across images.
  2. Pixel-wise Segmentation: Annotators utilize advanced tools to trace the exact contour and interior of the lips, differentiating them from the surrounding facial regions.
  3. Validation: A secondary set of experts review each annotation to confirm accuracy.

Annotation Metrics

  • Total Pixel-wise Annotations: 25,000 (One for each image)
  • Average Annotation Time per Image: 7 minutes (Given the detailed nature of lip boundaries)
Lips Segmentation Dataset
Lips Segmentation Dataset
Lips Segmentation Dataset
Lips Segmentation Dataset

Quality Assurance

Stages

Automated Model Verification: Preliminary lip segmentation models cross-check annotations to identify potential discrepancies.
Peer Review: Experienced annotators oversee a random sample to maintain a consistent quality benchmark.
Inter-annotator Agreement: A subset of images is annotated multiple times to ensure consistency and clarity in the dataset’s guidelines.

QA Metrics

  • Annotations Reviewed using Models: 12,500 (50% of total images)
  • Peer-reviewed Annotations: 7,500 (30% of total images)
  • Inconsistencies Identified and Addressed: 250 (1% of total images)

Conclusion

The Lips Segmentation Dataset serves as a cornerstone for a myriad of applications, from virtual makeup trials to health diagnostics. Its extensive array of images and meticulous annotations ensure that AI models trained on it will have an unparalleled understanding of human lips in all their multifaceted roles and expressions.

quality dataset

Quality Data Creation

Guaranteed TAT‚Äč

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified‚Äč

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance‚Äč

HIPAA Compliance

GDPR Compliance‚Äč

GDPR Compliance

Compliance and Security‚Äč

Compliance and Security

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