Introduction

Code for 2016 TPAMI(IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE) A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

Data

Prepare the dataset

get gait dataset from http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitLP.html, use the Version 1, OULP-C1V1, http://www.am.sanken.osaka-u.ac.jp/BiometricDB/doc/OULP_Doc01a_SubsetStatistics_OULP-C1V1.pdf, download the dataset into path: ~/data/, rename the directory to OULP_C1V1_Pack, you can see directories OULP-C1V1_NormalizedSilhouette(88x128) and OULP-C1V1_SubjectIDList(FormatVersion1.0) in ~/data/OULP_C1V1_Pack.

Prepare training, validation and test dataset

protocol, described in paper: Cross-View Gait Recognition Using View-Dependent Discriminative Analysis. In this experiments, training set described in the paper was split into traning and validation part, in OULP_setting/list_train.txt and OULP_setting/list_val.txt

1. change current directory to here
2. move OULP_setting directory to ~/data/OULP_setting, run command line: cp -r OULP_setting ~/data/OULP_setting
3. run command line:python preprocess_script/oulp_prepare.py ~/data/gait-oulp-c1v1

Running the code

Command line example to run the code

th main.lua -datapath ~/data/gait-oulp-c1v1 -mode train  -modelname wuzifeng -gpu -gpudevice 1  -dropout 0.5 -learningrate 1e-3 -momentum 0.9 -calprecision 2 -calval 1 -batchsize 64 -iteration 2000000 >> main.lua.log

Result

you will see the validation average precision up to 92.50.

you will see results in main.lua.log which like below:

{
  iteration : 2000000
  seed : 1
  loadmodel : ""
  datapart : "test"
  batchsize : 64
  debug : false
  gpu : true
  gpudevice : 1
  modelname : "wuzifeng"
  calval : 1
  momentum : 0.9
  datapath : "/home/chenqiang/data/gait-oulp-c1v1"
  gradclip : 5
  dropout : 0.5
  learningrate : 0.001
  calprecision : 2
  mode : "train"
}
2017-05-23 15:53:51[INFO] load data from /home/chenqiang/data/gait-oulp-c1v1/oulp_train_data.txt, /home/chenqiang/data/gait-oulp-c1v1/oulp_val_data.txt, /home/chenqiang/data/gait-oulp-c1v1/oulp_test_data.txt	
2017-05-23 15:53:51[INFO] train data instances 06848, uniq  0856	
2017-05-23 15:53:51[INFO] train data instances 00800, uniq  0100	
2017-05-23 15:53:51[INFO] train data instances 07648, uniq  0956	
nn.Sequential {
  [input -> (1) -> (2) -> (3) -> output]
  (1): nn.ParallelTable {
    input
      |`-> (1): nn.Sequential {
      |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> output]
      |      (1): nn.SpatialConvolutionMM(1 -> 16, 7x7)
      |      (2): nn.ReLU
      |      (3): nn.SpatialCrossMapLRN
      |      (4): nn.SpatialMaxPooling(2x2, 2,2)
      |      (5): nn.SpatialConvolutionMM(16 -> 64, 7x7)
      |      (6): nn.ReLU
      |      (7): nn.SpatialCrossMapLRN
      |      (8): nn.SpatialMaxPooling(2x2, 2,2)
      |    }
       `-> (2): nn.Sequential {
             [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> output]
             (1): nn.SpatialConvolutionMM(1 -> 16, 7x7)
             (2): nn.ReLU
             (3): nn.SpatialCrossMapLRN
             (4): nn.SpatialMaxPooling(2x2, 2,2)
             (5): nn.SpatialConvolutionMM(16 -> 64, 7x7)
             (6): nn.ReLU
             (7): nn.SpatialCrossMapLRN
             (8): nn.SpatialMaxPooling(2x2, 2,2)
           }
       ... -> output
  }
  (2): nn.Sequential {
    [input -> (1) -> (2) -> output]
    (1): nn.CSubTable
    (2): nn.Abs
  }
  (3): nn.Sequential {
    [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
    (1): nn.SpatialConvolutionMM(64 -> 256, 7x7)
    (2): nn.Reshape(1x112896)
    (3): nn.Dropout(0.5, busy)
    (4): nn.Linear(112896 -> 2)
    (5): nn.LogSoftMax
  }
}
2017-05-23 15:53:52[INFO] Number of parameters:1079906	
2017-05-23 15:54:02[INFO] 00001th/2000000 Val Error 0.693167	
2017-05-23 15:54:12[INFO] 00001th/2000000 Tes Error 0.693317	
2017-05-23 15:54:21[INFO] 00001th/2000000 Tra Error 0.693182, 29	
2017-05-23 15:54:31[INFO] 00065th/2000000 Val Error 0.693064	
2017-05-23 15:54:40[INFO] 00065th/2000000 Tes Error 0.693260	
2017-05-23 15:54:49[INFO] 00065th/2000000 Tra Error 0.693897, 27	

Test

select the model file in trainedNet as a argument of -loadmodel when run main.lua, then you can see the test result in the redirected file. the best average recognition precision you can get is 88.29.

th main.lua -datapath ~/data/gait-oulp-c1v1 -mode evaluate -datapart test  -modelname wuzifeng -gpu -gpudevice 1 -loadmodel ./trainedNets/wuzifeng_tra_0.6666_i7745.t7 >> main.lua.result.log