说明

本仓库修改于仓库：face_classification

原仓库使用 OpenCV 进行人脸检测，但准确度不够高，因此后改用 MTCNN 增加检测准确率。

在原仓库中的改动如下：

• 增加 MTCNN 模型： haarcascade_frontalface_default.xml

• 增加人脸检测程序：detect_face.py

• 更改程序 video_emotion_gender_demo.py, 使用 detect_face 来检测人脸。

下面是原始 README 内容：

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Face classification and detection from the B-IT-BOTS robotics team.

Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV.

• IMDB gender classification test accuracy: 96%.
• fer2013 emotion classification test accuracy: 66%.

For more information please consult the publication

Emotion/gender examples:

Guided back-prop

Real-time demo:

B-IT-BOTS robotics team :)

Instructions

Run real-time emotion demo:

python3 video_emotion_color_demo.py

Run real-time guided back-prop demo:

python3 image_gradcam_demo.py

Make inference on single images:

python3 image_emotion_gender_demo.py <image_path>

e.g.

python3 image_emotion_gender_demo.py ../images/test_image.jpg

Running with Docker

With a few steps one can get its own face classification and detection running. Follow the commands below:

• docker pull ekholabs/face-classifier
• docker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifier
• curl -v -F image=@[path_to_image] http://localhost:8084/classifyImage > image.png

To train previous/new models for emotion classification:

• Download the fer2013.tar.gz file from here

• Move the downloaded file to the datasets directory inside this repository.

• Untar the file:

tar -xzf fer2013.tar

• Run the train_emotion_classification.py file

python3 train_emotion_classifier.py

To train previous/new models for gender classification:

• Download the imdb_crop.tar file from here (It's the 7GB button with the tittle Download faces only).

• Move the downloaded file to the datasets directory inside this repository.

• Untar the file:

tar -xfv imdb_crop.tar

• Run the train_gender_classification.py file

python3 train_gender_classifier.py