Face Parsing Dataset

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Project Overview:

Objective

Face Parsing Dataset: Our goal was to create a dataset that meticulously dissects human facial features, including eyes, nose, mouth, and skin. This precision-focused dataset aids significantly in enhancing the accuracy of facial recognition software and the realism of augmented reality applications.

Scope

We embarked on an extensive data collection journey, gathering a wide range of human facial images and providing detailed annotations for each facial feature.

Sources

Public datasets of human portraits.
Selfies and profile pictures from social media, were obtained with explicit permission.
Our studio captures, featuring diverse lighting, angles, and expressions.
CCTV footage, used in full compliance with legal requirements.

Data Collection Metrics

Total Data Points: 200,000 facial images
Studio Captures: 50,000
Social Media Extracts: 80,000
Public Datasets: 50,000
CCTV Footage: 20,000

Annotation Process

Stages

Face Detection: Our algorithms proficiently identified and outlined faces in images.
Feature Segmentation: We split faces into segments like eyes, nose, mouth, eyebrows, ears, and hair.
Sub-segmentation: For detailed analysis, parts of the eye, for example, were divided further.
Attribute Annotation: Each facial feature was classified based on specific attributes.

Annotation Metrics

Total Annotations: 1,600,000
Faces Detected: 200,000
Feature Segmentations: 1,200,000 (approx. 6 per face)
Sub-segmentations: 100,000 (selected features)
Attribute Annotations: 100,000

Quality Assurance

Stages

Expert Review: A panel of dermatologists and makeup artists evaluated a portion of the annotations.
Automated Consistency Checks: Our algorithms diligently identified annotation inconsistencies.
Inter-annotator Agreement: Ensuring consistent tagging by overlapping data subsets among annotators.

QA Metrics

Annotations Reviewed by Experts: 160,000
Inconsistencies Identified and Corrected: 8,000

Conclusion

The Face Parsing Dataset project successfully curated a comprehensive database ideal for training and testing advanced facial recognition and augmented reality systems. Rigorous standards for collection and annotation ensured that the dataset stands as a premium resource for innovators in technology and research fields.

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