Facial Parts Semantic Segmentation Dataset

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Project Overview:

Objective

To develop a comprehensive dataset focused on semantic segmentation of individual facial parts, enabling advancements in face recognition, emotion detection, augmented reality, and more.

Scope

Capture a variety of facial images that show different expressions, angles, lighting conditions, and demographics. Make sure to include detailed annotations for facial features like the eyes, nose, mouth, eyebrows, and skin.

Sources

Studio photographs with controlled lighting.
Candid shots from public events.
Selfies and close-up shots from personal devices.
Images encompassing a wide range of ages, ethnicities, and skin types.
Different facial expressions: smiling, frowning, surprised, etc.

Data Collection Metrics

Total Data Points: 350,000 images
Studio Photographs: 100,000
Candid Shots: 75,000
Selfies: 100,000
Diverse Demographics: 75,000

Annotation Process

Stages

Raw Data Cleaning: First, we will remove any images that are blurry, have obstructions, or do not focus on the face.
Major Facial Features Segmentation: Next, we will annotate the main features, such as the eyes, nose, mouth, and eyebrows.
Detailed Segmentation: Then, we will go deeper by marking areas like the upper and lower eyelid, nostrils, upper and lower lip, individual eyebrows, and more.
Quality Review: Finally, we will perform strict checks to ensure that the annotations are consistent and accurate.

Annotation Metrics

Total Major Feature Segmentations: 1,400,000 (4 segments per image on average)
Total Detailed Segmentations: 2,100,000 (6 detailed segments per image on average)
Annotations Reviewed: 350,000 (100% major feature review for accuracy)

Quality Assurance

Stages

Expert Review: Professionals specializing in facial anatomy and dermatology reviewed the dataset.
Automated Consistency Checks: Algorithms cross-checked annotations to identify potential discrepancies.
Inter-annotator Agreement: Multiple annotators worked on overlapping image sets to ensure uniformity.

QA Metrics

Annotations Reviewed by Experts: 70,000 (20% of total annotations)
Inconsistencies Identified and Rectified: 14,000 (4% of total annotations)

Conclusion

The Facial Parts Semantic Segmentation Dataset is set to be a valuable resource in many tech fields. For instance, it can enhance facial recognition algorithms and improve augmented reality facial filters. By focusing on small details of the face and capturing a wide range of human diversity, this dataset offers unmatched detail and accuracy for related uses.