SSR-Net

[IJCAI18] SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation

• A real-time age estimation model with 0.32MB.

Code Author: Tsun-Yi Yang

Last update: 2018/05/14 (paper format update)

Paper

PDF

https://github.com/shamangary/SSR-Net/blob/master/ijcai18_ssrnet_pdfa_2b.pdf

Paper authors

Tsun-Yi Yang, Yi-Husan Huang, Yen-Yu Lin, Pi-Cheng Hsiu, and Yung-Yu Chuang

Abstract

This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size. Inspired by DEX, we address age estimation by performing multi-class classification and then turning classification results into regression by calculating the expected values. SSR-Net takes a coarse-to-fine strategy and performs multi-class classification with multiple stages. Each stage is only responsible for refining the decision of the previous stage. Thus, each stage performs a task with few classes and requires few neurons, greatly reducing the model size. For addressing the quantization issue introduced by grouping ages into classes, SSR-Net assigns a dynamic range to each age class by allowing it to be shifted and scaled according to the input face image. Both the multi-stage strategy and the dynamic range are incorporated into the formulation of soft stagewise regression. A novel network architecture is proposed for carrying out soft stagewise regression. The resultant SSR-Net model is very compact and takes only 0.32 MB. Despite of its compact size, SSR-Net’s performance approaches those of the state-of-the-art methods whose model sizes are more than 1500x larger.

Platform

• Keras
• Tensorflow
• GTX-1080Ti
• Ubuntu

Dependencies

• A guide for most dependencies. (in Chinese) http://shamangary.logdown.com/posts/3009851
• OpenCV
• dlib
• MTCNN for demo

pip install mtcnn

• MobileNet (already in the codes) https://github.com/fchollet/keras/blob/master/keras/applications/mobilenet.py
• DenseNet (already in the codes) https://github.com/titu1994/DenseNet
• Face alignment (already in the codes) https://github.com/xyfeng/average_portrait
• Others

conda install -c conda-forge moviepy
conda install -c cogsci pygame

Codes

There are three different section of this project. 1.Data pre-processing 2.Training and testing 3.Video demo section We will go through the details in the following sections.

This repository is for IMDB, WIKI, and Morph2 datasets. Megaface-Asian is done by Yi-Husan Huang. Another link for this will be updated soon.

1. Data pre-processing

• Download IMDB-WIKI dataset (face only) from https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/.
• Morph2 dataset requires application form https://www.faceaginggroup.com/morph/
• Unzip them under './data'
• Run the following codes for dataset pre-processing.

cd ./data
python TYY_IMDBWIKI_create_db.py --db imdb --output imdb.npz
python TYY_IMDBWIKI_create_db.py --db wiki --output wiki.npz
python TYY_MORPH_create_db.py --output morph_db_align.npz

2. Training and testing

The experiments are done by randomly choosing 80% of the dataset as training and 20% of the dataset as validation(or testing). The details of the setting in each dataset is in the paper.

For MobileNet and DenseNet:

cd ./training_and_testing
sh run_all.sh

For SSR-Net:

cd ./training_and_testing
sh run_ssrnet.sh

• Note that we provide several different hyper-parameters combination in this code. If you only want a single hyper-parameter set, please alter the command inside "run_ssrnet.sh".

Plot the results: For example, after the training of IMDB dataset, you want to plot the curve and the results. Copy "plot.sh", "ssrnet_plot.sh", and "plot_reg.py" into "./imdb_models". The following command should plot the results of the training process.

sh plot.sh
sh ssrnet_plot.sh

3. Video demo section

Pure CPU demo command:

cd ./demo
KERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_mtcnn.py TGOP.mp4

• Note: You may choose different pre-trained models. However, the morph2 dataset is under a well controlled enviroment and it is much more smaller than IMDB and WIKI, the pre-trained models from morph2 may perform ly on the in-the-wild images. Therefore, IMDB or WIKI pre-trained models are recommended for in-the-wild images or video demo.

• We use dlib detection and face alignment in the previous experimental section since the face data is well organized. However, dlib cannot provide satisfactory face detection for in-the-wild video. Therefore we use mtcnn as the detection process in the demo section.