/SSR-Net

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

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SSR-Net

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

Code Author: Tsun-Yi Yang

Last update: 2019/09/19 (Renew the morph2 dataset link)

Real-time webcam demo

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

pip install mtcnn

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

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.

1. Data 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

# Or you can use

KERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_mtcnn.py TGOP.mp4 '3'

  • Note: You may choose different pre-trained models. However, the morph2 dataset is under a well controlled environment 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.

Real-time webcam demo

Considering the face detection process (MTCNN or Dlib) is not fast enough for real-time demo. We show a real-time webcam version by using lbp face detector.

cd ./demo
KERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_ssrnet_lbp_webcam.py
  • Note that the covered region of face detection is different when you use MTCNN, Dlib, or LBP. You should choose similar size between the inference and the training.
  • Also, the pre-trained models are mainly for the evaluation of the datasets. They are not really for the real-world images. You should always retrain the model by your own dataset. In webcam demo, we found that morph2 pre-trained model actually perform better than wiki pre-trained model. The discussion will be included in our future work.
  • If you are Asian, you might want to use the megaage_asian pre-trained model.
  • The Morph2 pre-trained model is good for webcam but the gender model is overfitted and not practical.

4. Extension

Training the gender model

We can reformulate binary classification problem into regression problem, and SSR-Net can be used to predict the confidence. For example, we provide gender regression and demo in the code for the extension.

Training the gender network:

cd ./training_and_testing
sh run_ssrnet_gender.sh

Note that the score can be between [0,1] and the 'V' inside SSR-Net can be changed into 1 for general propose regression.

Third Party Implementation