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opencv/
facial_expression_recognition

onnx

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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:

shell

# 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:

shell

# 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

opencv/facial_expression_recognition