This is a Keras implementation of a CNN for facial attribute recognition. I trained Visual Transformer and facenet for facial attribute extraction.
- Python3.6+
- Ubuntu 16.04, Python 3.6.9, Tensorflow 2.3.0, CUDA 10.01, cuDNN 7.6
I trained the face attribute extraction models with CelebFaces Attributes (CelebA) Dataset
You can download the preprocessed dataset from the below link. I cropped the faces and converted them into RGB format. The dataset contains 100000 images with facial attributes. https://drive.google.com/drive/folders/1iffYL-rB-3MbqI-TfFFHU6Wc-JaYHgGz?usp=sharing
python train.py --imagepath=/data/imageFile100000.npz --labelpath=/data/labelFile100000.npz
python demo.py
python realtime_testing.py
Please use the below weights for testing. https://drive.google.com/drive/folders/1NWz2E3b75mO_Ox8tb9d77vBi8dNHUv1T?usp=sharing
| Model | Train Accuracy | Validation Accuracy | Test Accuracy |
|---|---|---|---|
| VIT | 81.2 | 82 | 81.28 |
| FaceNet | 84.5 | 85.71 | 86.25 |
| InclusiveFaceNet | 90.96 |
I used the bigbangtheory cast image as a testing image. Please find the person's result.
- Step 1: Download the Pretrained facenet model and create the new folder inside the DockerFiles place it. i.e /DockerFiles/models/
/DockerFiles/models/model_inception_facial_keypoints.h5
- Step 2: Use the below command for docker compose.
docker-compose up -d
- Step 3: Run the following the command to build the docker image.
docker build -t facial_attribute .
- Step 4: Start the detection service.
docker run -it facial_attribute
- Step 5: Pass the image for testing.
curl -X POST -F 'file=@/home/DSN/Desktop/1.jpg' http://172.17.0.2:5000/
- Step 6: JSON results format:
{
"result": [
{
"coord": [
607,
65,
711,
169
],
"face": [
{
"label": "Attractive",
"prob": "0.5497437"
},
{
"label": "Male",
"prob": "0.8896191"
},
{
"label": "No_Beard",
"prob": "0.92911637"
},
{
"label": "Young",
"prob": "0.92061347"
}
]
},
{
"coord": [
1149,
131,
1235,
218
],
"face": [
{
"label": "Big_Nose",
"prob": "0.5611748"
},
{
"label": "Male",
"prob": "0.96252704"
},
{
"label": "Mouth_Slightly_Open",
"prob": "0.78494644"
},
{
"label": "No_Beard",
"prob": "0.5100374"
},
{
"label": "Smiling",
"prob": "0.7040582"
},
{
"label": "Young",
"prob": "0.8379371"
}
]
},
{
"coord": [
803,
150,
889,
237
],
"face": [
{
"label": "Attractive",
"prob": "0.59744525"
},
{
"label": "Heavy_Makeup",
"prob": "0.552807"
},
{
"label": "No_Beard",
"prob": "0.986242"
},
{
"label": "Wearing_Lipstick",
"prob": "0.692116"
},
{
"label": "Young",
"prob": "0.93902016"
}
]
},
{
"coord": [
976,
141,
1062,
227
],
"face": [
{
"label": "Eyeglasses",
"prob": "0.6799438"
},
{
"label": "Male",
"prob": "0.7749488"
},
{
"label": "No_Beard",
"prob": "0.9461371"
},
{
"label": "Young",
"prob": "0.6490406"
}
]
},
{
"coord": [
179,
150,
266,
237
],
"face": [
{
"label": "Male",
"prob": "0.9068607"
},
{
"label": "Mouth_Slightly_Open",
"prob": "0.91096807"
},
{
"label": "No_Beard",
"prob": "0.623013"
},
{
"label": "Smiling",
"prob": "0.8010901"
},
{
"label": "Wearing_Hat",
"prob": "0.57096326"
},
{
"label": "Young",
"prob": "0.88812125"
}
]
},
{
"coord": [
446,
158,
549,
261
],
"face": [
{
"label": "Big_Nose",
"prob": "0.7039994"
},
{
"label": "Eyeglasses",
"prob": "0.87806904"
},
{
"label": "High_Cheekbones",
"prob": "0.596"
},
{
"label": "Male",
"prob": "0.9493711"
},
{
"label": "Mouth_Slightly_Open",
"prob": "0.82170117"
},
{
"label": "No_Beard",
"prob": "0.86256987"
},
{
"label": "Smiling",
"prob": "0.88448894"
}
]
},
{
"coord": [
304,
170,
390,
256
],
"face": [
{
"label": "Bangs",
"prob": "0.56573707"
},
{
"label": "Eyeglasses",
"prob": "0.65550697"
},
{
"label": "High_Cheekbones",
"prob": "0.67516124"
},
{
"label": "Mouth_Slightly_Open",
"prob": "0.8242004"
},
{
"label": "No_Beard",
"prob": "0.9694848"
},
{
"label": "Smiling",
"prob": "0.8470793"
},
{
"label": "Young",
"prob": "0.6668907"
}
]
}
]
}
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