Jia-Hong Lee, Yi-Ming Chan, Ting-Yen Chen, and Chu-Song Chen, "Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications," IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2018
Official implementation of Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
Created by Jia-Hong Lee, Yi-Ming Chan,Ting-Yen Chen, Chu-Song Chen
Automatic age and gender classification based on unconstrained images has become essential techniques on mobile devices. With limited computing power, how to develop a robust system becomes a challenging task. In this paper, we present an efficient convolutional neural network (CNN) called lightweight multi-task CNN for simultaneous age and gender classification. Lightweight multi-task CNN uses depthwise separable convolution to reduce the model size and save the inference time. On the public challenging Adience dataset, the accuracy of age and gender classification is better than baseline multi-task CNN methods.
If you find our works useful in your research, please consider citing:
@inproceedings{lee2018joint,
title={Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications},
author={Lee, Jia-Hong and Chan, Yi-Ming and Chen, Ting-Yen and Chen, Chu-Song},
booktitle={2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)},
pages={162--165},
year={2018},
organization={IEEE}
}
$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
$ git clone --recursive https://github.com/ivclab/agegenderLMTCNN.git
$ python download_adiencedb.py
$ python datapreparation.py \
--inputdir=./adiencedb/aligned \
--rawfoldsdir=./DataPreparation/FiveFolds/original_txt_files \
--outfilesdir=./DataPreparation/FiveFolds/train_val_test_per_fold_agegender
$ python multipreproc.py \
--fold_dir ./DataPreparation/FiveFolds/train_val_test_per_fold_agegender \
--data_dir ./adiencedb/aligned \
--tf_output_dir ./tfrecord \
or you can download tfrecord files which have been generated:
$ python download_tfrecord.py
or you can download the tfrecord files on OneDrive
# five-fold LMTCNN model for age and gender tasks
$ ./script/trainfold1_best.sh ~ $ ./script/trainfold5_best.sh
# five-fold Levi_Hassner model for age task
$ ./script/trainagefold1.sh ~ $ ./script/trainagefold5.sh
# five-fold Levi_Hassner model for gender task
$ ./script/traingenderfold1.sh ~ $ ./script/traingenderfold5.sh
or you can download model files which have been generated:
$ python download_model.py
or you can download the model files on OneDrive
# five-fold LMTCNN model for age and gender tasks
$ ./script/evalfold1_best.sh ~ $ ./script/evalfold5_best.sh
# five-fold Levi_Hassner model for age task
$ ./script/evalagefold1.sh ~ $ ./script/evalagefold5.sh
# five-fold Levi_Hassner model for gender task
$ ./script/evalgenderfold1.sh ~ $ ./script/evalgenderfold5.sh
# five-fold LMTCNN model for age and gender tasks
$ ./script/inference1_best.sh ~ $ ./script/inference5_best.sh
# five-fold Levi_Hassner model for age task
$ ./script/inferenceage1.sh ~ $ ./script/inferenceage5.sh
# five-fold Levi_Hassner model for gender task
$ ./script/inferencegender1.sh ~ $ ./script/inferencegender5.sh
Age and Gender Classification using Convolutional Neural Networks(https://www.openu.ac.il/home/hassner/projects/cnn_agegender/)
Please feel free to leave suggestions or comments to Jia-Hong Lee([email protected]), Yi-Ming Chan ([email protected]), Ting-Yen Chen ([email protected]) ,Chu-Song Chen ([email protected])