facial_expression_recognition

Progressive Teacher

Progressive Teacher: Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher

Note:

• Progressive Teacher is contributed by Jing Jiang.
• MobileFaceNet is used as the backbone and the model is able to classify seven basic facial expressions (angry, disgust, fearful, happy, neutral, sad, surprised).
• facial_expression_recognition_mobilefacenet_2022july.onnx is implemented thanks to Chengrui Wang.
• facial_expression_recognition_mobilefacenet_2022july_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.

Results of accuracy evaluation on RAF-DB.

Models	Accuracy
Progressive Teacher	88.27%

Demo

NOTE: This demo uses ../face_detection_yunet as face detector, which supports 5-landmark detection for now (2021sep).

Python

Run the following command to try the demo:

# recognize the facial expression on images
python demo.py --input /path/to/image -v

C++

Install latest OpenCV and CMake >= 3.24.0 to get started with:

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/opencv_zoo_face_expression_recognition
# detect on an image
./build/opencv_zoo_face_expression_recognition -i=/path/to/image
# get help messages
./build/opencv_zoo_face_expression_recognition -h

Example outputs

Note: Zoom in to to see the recognized facial expression in the top-left corner of each face boxes.

License

All files in this directory are licensed under Apache 2.0 License.

Reference

• https://ieeexplore.ieee.org/abstract/document/9629313