Age and Gender Estimation

This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used.

Dependencies

Python3.5+
Keras2.0+
scipy, numpy, Pandas, tqdm, tables, h5py
dlib (for demo)
OpenCV3

Tested on:

Ubuntu 16.04, Python 3.5.2, Keras 2.0.3, Tensorflow(-gpu) 1.0.1, Theano 0.9.0, CUDA 8.0, cuDNN 5.0
- CPU: i7-7700 3.60GHz, GPU: GeForce GTX1080
macOS Sierra, Python 3.6.0, Keras 2.0.2, Tensorflow 1.0.0, Theano 0.9.0

Usage

Use pretrained model for demo

Run demo the script (requires web cam)

python3 demo.py

The pretrained model for TensorFlow backend will be automatically downloaded to the pretrained_models directory.

Train a model using the IMDB-WIKI dataset

Download the dataset

The dataset is downloaded and extracted to the data directory.

./download.sh

Create training data

Filter out noise data and serialize images and labels for training into .mat file. Please check check_dataset.ipynb for the details of the dataset.

python3 create_db.py --output data/imdb_db.mat --db imdb --img_size 64

usage: create_db.py [-h] --output OUTPUT [--db DB] [--img_size IMG_SIZE] [--min_score MIN_SCORE]

This script cleans-up noisy labels and creates database for training.

optional arguments:
  -h, --help                 show this help message and exit
  --output OUTPUT, -o OUTPUT path to output database mat file (default: None)
  --db DB                    dataset; wiki or imdb (default: wiki)
  --img_size IMG_SIZE        output image size (default: 32)
  --min_score MIN_SCORE      minimum face_score (default: 1.0)

Train network

Train the network using the training data created above.

python3 train.py --input data/imdb_db.mat

Trained weight files are stored as checkpoints/weights.*.hdf5 for each epoch if the validation loss becomes minimum over previous epochs.

usage: train.py [-h] --input INPUT [--batch_size BATCH_SIZE]
                [--nb_epochs NB_EPOCHS] [--depth DEPTH] [--width WIDTH]
                [--validation_split VALIDATION_SPLIT] [--aug]

This script trains the CNN model for age and gender estimation.

optional arguments:
  -h, --help            show this help message and exit
  --input INPUT, -i INPUT
                        path to input database mat file (default: None)
  --batch_size BATCH_SIZE
                        batch size (default: 32)
  --nb_epochs NB_EPOCHS
                        number of epochs (default: 30)
  --depth DEPTH         depth of network (should be 10, 16, 22, 28, ...)
                        (default: 16)
  --width WIDTH         width of network (default: 8)
  --validation_split VALIDATION_SPLIT
                        validation split ratio (default: 0.1)
  --aug                 use data augmentation if set true (default: False)

Train network with recent data augmentation methods

Recent data augmentation methods, mixup [3] and Random Erasing [4], can be used with standard data augmentation by --aug option in training:

python3 train.py --input data/imdb_db.mat --aug

Please refer to this repository for implementation details.

I confirmed that data augmentation enables us to avoid overfitting and improves validation loss.

Use the trained network

python3 demo.py

usage: demo.py [-h] [--weight_file WEIGHT_FILE] [--depth DEPTH] [--width WIDTH]

This script detects faces from web cam input, and estimates age and gender for
the detected faces.

optional arguments:
  -h, --help                show this help message and exit
  --weight_file WEIGHT_FILE path to weight file (e.g. weights.18-4.06.hdf5) (default: None)
  --depth DEPTH             depth of network (default: 16)
  --width WIDTH             width of network (default: 8)

Please use the best model among checkpoints/weights.*.hdf5 for WEIGHT_FILE if you use your own trained models.

Plot training curves from history file

python3 plot_history.py --input models/history_16_8.h5

Results without data augmentation

Results with data augmentation

The best val_loss was improved from 3.969 to 3.731:

Without data augmentation: 3.969
With standard data augmentation: 3.799
With mixup and random erasing: 3.731

We can see that, with data augmentation, overfitting did not occur even at very small learning rates (epoch > 15).

Network architecture

In the original paper [1, 2], the pretrained VGG network is adopted. Here the Wide Residual Network (WideResNet) is trained from scratch. I modified the @asmith26's implementation of the WideResNet; two classification layers (for age and gender estimation) are added on the top of the WideResNet.

Note that while age and gender are independently estimated by different two CNNs in [1, 2], in my implementation, they are simultaneously estimated using a single CNN.

Results

Trained on imdb, tested on wiki.

References

[1] R. Rothe, R. Timofte, and L. V. Gool, "DEX: Deep EXpectation of apparent age from a single image," ICCV, 2015.

[2] R. Rothe, R. Timofte, and L. V. Gool, "Deep expectation of real and apparent age from a single image without facial landmarks," IJCV, 2016.

[3] H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, "mixup: Beyond Empirical Risk Minimization," in arXiv:1710.09412, 2017.

[4] Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, "Random Erasing Data Augmentation," in arXiv:1708.04896, 2017.

hehuanshu96/age-gender-estimation