Guideline

This repo includes 3 main parts: using deepface to output age, gender, race, emotion and 2 models training from scratch are masked detection model and skintone detection model. All code are trained and evaluated on Kaggle with all provided data uploaded there.

DeepFace

Running experiment.ipynb notebook on Kaggle with public and private test data to get multiple backend detectors output for age, gender, race, emotion.

Skintone detection

In folder skinton_detection, run the skintone_analysis.ipynb notebook to train and predict skintone feature for public and private test data. A model skin_classify_150epoch.keras will be saved to Kaggle working directory /kaggle/working after training, which can be used for inference and fine-tuning later.

Masked detection

In folder masked_detection, run the masked_model_training.ipynb to train the masked detection model. A model named my_model.keras will be saved to Kaggle working directory /kaggle/working.

About Dataset

Total images: 15000 images (jpg format) including real images and synthetic images
Number of images with more than 1 face: 109 images (0.7%)
File annotation format:
CSV file
15310 rows
Main attributes:
- bbox: face bounding box of format (x, y, w, h) IOU (AVG precision IOU threshold từ 0.5 - 0.95)
- age: 20-30s, 40-50s, Kid, Senior, Baby, Teenager - accuracy (*)
- race: Caucasian, Mongoloid, Negroid - accuracy (*)
- masked: Unmasked, Masked - accuracy
- skintone: Mid-light, Light, Mid-dark, Dark - accuracy (*)
- emotion: Neutral, Happiness, Anger, Surprise, Fear, Sadness, Disgust - accuracy (*)
- gender: Male, Female - accuracy
Các thuộc tính khó: Độ tuổi, Cảm xúc, Tông màu da, Chủng tộc sẽ được đánh giá cao hơn các thuộc tính khác

Reference

Masked detection:

- https://medium.com/cloudnloud/building-a-face-mask-detector-using-python-and-opencv-2654e28d8d76