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https://github.com/ayulockin/humain_faceless

Detect faces in an image and give prediction about the age, ethnicity and emotion of that face.
https://github.com/ayulockin/humain_faceless

deeplearning face-detection image-processing keras mtcnn-face-detection multiclass-classification multilabel-classification opencv tensorflow

Last synced: 11 days ago
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Detect faces in an image and give prediction about the age, ethnicity and emotion of that face.

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README 

        
# humAIn_FaceLess



## Installations



Since git LFS wasn't used huge files weren't provided along with this repository. Thus follow these steps to access all the functionality provided with this repository. 



### Download Images



1. Clone this repository to you local system.



2. The dataset provided by the HumAIn team is in `JSON` format. The same can be found in the `datasets` dir. I have added a parent key named `data`so to the provided data. If you have a test dataset in `JSON` and want to use the same for evaluation make sure to add this parent key.



3. To download images open `command prompt` or `terminal`in the root of the cloned repository. 



   ```bash

   python data_download.py -d datasets/Face_Recognition.json

   ```



4. To create a cleaner `json` file with annotations.



   ```

   python data_download.py -d datasets/Face_Recognition.json -n True

   ```



### Download Weights



Download my trained model `weight files` from [here](https://drive.google.com/drive/folders/15YPWuZU17Tp6oi2i5vdUlxt26HKiKqyp?usp=sharing). The files should be in this relative path: `face_detector\weights`



### Download Additional Dataset (Optional)



1. In order to train my model for Age, Gender and Ethnicity prediction, I have used [UTKFace](https://susanqq.github.io/UTKFace/) dataset along side the data provided.



2. In order to train my model for Emotion prediction I have used the dataset from [this](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data ) Kaggle competition.



   You can run `prepareUTKFaceData.py`(Optional)



   ```bash

   python prepareUTKFaceData.py -d path_to_downloaded_UTKFace_dataset

   ```



   This will create a `face_dataset.csv` dataset along with a directory in the root named `UTKFaceimages`. The `face_dataset.csv` contains `image_id, age, gender, ethnicity` as input image name along with class label. The `UTKFaceimages` directory contain images with `image_id`. This renaming of file is done as a part of data preprocessing which makes it easier while preparing data for training. 



## USAGE



Go to the root of this repository and open command prompt or in your terminal run `main.py` script. You need to specify the path to the test image. 



```bash

python main.py -i examples/example1.jpeg

```



If you want to see the bounding box detected you can run this. 



```bash

python main.py -i examples/example1.jpeg -b True

```



This will do the following:



1. Will import all the packages.

2. Will build the model and load weights. This may take some time in the first time. 

3. With the given input image run the inference through Face Detector. This will output bounding boxes of the detected faces.

4. It will use this output and crop the faces from the input image which will be the input to three parallel detectors. 

5. The result of the detector will be printed on the terminal.



You can find few other example images to test with. 



# Model



![architecture](examples/humAIn-Faceless.png)