Skip to content

Latest commit

 

History

History
42 lines (34 loc) · 3.3 KB

README.md

File metadata and controls

42 lines (34 loc) · 3.3 KB
Raw

Light CNN for Deep Face Recognition

Install Required Packages

First ensure that you have installed the following required packages:

See requirements.txt for details.

Datasets

  • In the implementation of the LightCNN, MsCelebV1-Faces-Aligned subset as the training data and validating data. Utilize the 10K, 70K download to generate the subset of the MsCelebV1-Faces-Aligned. Then, use the mxnet tools im2rec.py to get the Record file, the image size is 144x144. When training LightCNN, all the training data are converted to gray-scale images and randomly croped to 128x128.

Training a Model

  • Run the following script to train and validate the model.
python train.py

You could change some arguments in the train.py, like num_epochs, gpus. If want to get the good results and save training time, there are some points should note:

  • For weights initialization, use "xavier" initialization for "weight" of every layer, the "bias" will initializate to constant value "0.1". See common/fit.py initializer for details.
  • For learning rate, use "AdamOptimizer" as optimization method, and initial learning rate is 0.00001. So in mxnet, you can set the initial learning rate is 0.0001, lr_step_epochs is '0, 100', in this way, the initial learning rate is 0.00001 in epoch 0.

Extract features of LFW

  • Run the following script to extract every image's feature, moreover save the features and image path to MATLAB data.
cd extract_features
python extract_features.py

The script will load the trained LightCNN model to generate fc layer output, w.t. the feature of a image. You could change the arguments in test.sh depend on your machine config. You could download the aligned LFW datasets and lfw_patch_part.txt from here download.

Face Verification

  • Utilize MATLAB to run the lfw_eval.m to verificate a pair of images. You must generate the features of the aligned LFW datasets in advance. See lfw_verification-matlab/lfw_eval.m for details.

Downloading trained model

  • Pretrained model: download. Training about 50 epoches use 10k people, the loss has converged. But the accuracy on the aligned LFW datasets is not high, about 85%.

  • Some improved solutions:

  1. Change the optimizer method, like: when loss doesn't decrease, manually decrease it to 10 time smaller.
  2. Improved by manaully aligning the images which are mis-algined in LFW datasets.
  3. Utilize a new feature of a image, for example use mfm_fc1_output as a image's feature, it will increase the accuracy in LFW.
  4. Improved by using metric learning method for similarity caculation, not just cos value.