EDCC: An efficient and accurate algorithm for palmprint-recognition

EDCC(Enhanced and Discriminative Competitive Code), which is used for palmprint-recognition.

Use the EDCC algorithm to validate on several published palmprint databases(multispectral, tongji), the first N(N = 2, 4, 6, 8) palmprint images of each palm are employed as training samples and the remaining palmprint images form the test sample set. Each sample in the test sample set is compared with all samples of each class in the training set to calculate the matching score. The class that produces the highest matching score is treated as the class of the test sample.

Database	N=2	N=4	N=6	N=8
Multispectral_B	98.6800%	99.8750%	99.9667%	99.9800%
Multispectral_G	98.8400%	99.8500%	99.9333%	99.9500%
Multispectral_I	98.9200%	99.9000%	99.9000%	99.9000%
Multispectral_R	98.8400%	99.7500%	99.8667%	99.9000%
Tongji	98.8056%	99.6979%	99.9881%	99.9861%

Advantages of EDCC algorithm:

1. Less training samples.
2. Faster recognition speed.
3. Higher recognition accuracy.

More details of EDCC

Installation

There are some requirements if you want to install EDCC:

1. Linux/Unix OS
2. OpenCV Installed

Steps:

1. git clone https://github.com/Leosocy/EDCC-Palmprint-Recognition.git
2. cd EDCC-Palmprint-Recognition && mkdir -p build && cd build
3. cmake ..
4. sudo make -j install

How to use EDCC in your project

There are some samples about how to use edcc api.

Usage of api.

• C++

  In your CMakeLists.txt, add these lines:

  FIND_PACKAGE(EDCC REQUIRED)
  INCLUDE_DIRECTORIES(${EDCC_INCLUDE_DIRS})
  TARGET_LINK_LIBRARIES(YOUR_TARGET_NAME ${EDCC_LIBS})

  Then you can use those apis in edcc.h like this:

  #include <edcc.h>
  int main(int argc, char** argv)
  {
      const char* palmprint_image_path_1 = "IMAGE_PATH_1";
      const char* palmprint_image_path_2 = "IMAGE_PATH_1";
      const char* config_file_path = "config.json";
      #define CODE_BUFFER_MAX_LEN 1024
      unsigned char code_buffer_1[CODE_BUFFER_MAX_LEN] = {0};
      unsigned char code_buffer_2[CODE_BUFFER_MAX_LEN] = {0};
      size_t buffer_len_1 = 0;
      size_t buffer_len_2 = 0;
      int ret = GetEDCCCoding(palmprint_image_path_1,
                              config_file_path,
                              CODE_BUFFER_MAX_LEN,
                              code_buffer_1,
                              &buffer_len_1);
      ret = GetEDCCCoding(palmprint_image_path_2,
                          config_file_path,
                          CODE_BUFFER_MAX_LEN,
                          code_buffer_2,
                          &buffer_len_2);
      double coding_matching_score = 0.0;
      double image_matching_score = 0.0;
      GetTwoPalmprintCodingMatchScore(code_buffer_1,
                                      code_buffer_2,
                                      &coding_matching_score);
      GetTwoPalmprintMatchScore(palmprint_image_path_1,
                                palmprint_image_path_2,
                                config_file_path,
                                &image_matching_score);
  }

• Python

  Python need 3.x.

  In your py, add these lines:

  from edcc_adapter import *
  
  api = EdccApi()
  coding_1, coding_len_1 = api.GetEDCCCoding("IMAGE_PATH_1", "CONFIG_PATH")
  coding_2, coding_len_2 = api.GetEDCCCoding("IMAGE_PATH_2", "CONFIG_PATH")
  
  coding_matching_score = api.GetTwoPalmprintCodingMatchScore(coding_1, coding_2)
  image_matching_score = api.GetTwoPalmprintMatchScore("IMAGE_PATH_1", "IMAGE_PATH_2", "CONFIG_PATH")

Run samples

Before running the example, make sure make install succeed.

• Run Python sample

  1. cd EDCC-Palmprint-Recognition && mkdir -p build && cd build
  2. cmake ..
  3. sudo make -j run_py_sample

  or you can use manage.sh to run samples, just like

  ./manage.sh run_py_sample

Contributing

Please see CONTRIBUTING.md