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

• A real-time age estimation model with 0.32MB.
• Gender regression is also added!
• Megaage-Asian is provided inhttps://github.com/b02901145/SSR-Net_megaage-asian
• Coreml model (0.17MB) is provided inhttps://github.com/shamangary/Keras-to-coreml-multiple-inputs-example

Code Author: Tsun-Yi Yang

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

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

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

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 only0.32 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.

• Keras
• Tensorflow
• GTX-1080Ti
• Ubuntu

• A guide for most dependencies. (in Chinese)http://shamangary.logdown.com/posts/3009851
• Anaconda
• 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 moviepyconda install -c cogsci pygameconda install -c conda-forge requestsconda install -c conda-forge pytables

There are three different section of this project.

1. Data pre-processing
2. Training and testing
3. Video demo sectionWe will go through the details in the following sections.

This repository is for IMDB, WIKI, and Morph2 datasets.

• Download IMDB-WIKI dataset (face only) fromhttps://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/.
• Morph2 dataset requires application formhttps://www.faceaginggroup.com/?page_id=1414https://ebill.uncw.edu/C20231_ustores/web/classic/product_detail.jsp?PRODUCTID=8
• Unzip them under './data'
• Run the following codes for dataset pre-processing.

cd ./datapython TYY_IMDBWIKI_create_db.py --db imdb --output imdb.npzpython TYY_IMDBWIKI_create_db.py --db wiki --output wiki.npzpython TYY_MORPH_create_db.py --output morph_db_align.npz

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_testingsh run_all.sh

For SSR-Net:

cd ./training_and_testingsh 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.shsh ssrnet_plot.sh

Pure CPU demo command:

cd ./demoKERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_mtcnn.py TGOP.mp4# Or you can useKERAS_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.

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 ./demoKERAS_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.

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_testingsh 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.

• MXNET:https://github.com/wayen820/gender_age_estimation_mxnet

• Pytorch:https://github.com/oukohou/SSR_Net_Pytorch

• Pytorch:https://github.com/CrazySummerday/SSR-Net