EDCC-Palmprint-Recognition

KO | EN

gitlite — search

#python #react #ai #typescript #golang #go #machine-learning #rust #docker #windows #kubernetes #awesome-list #vue #openai

EDCC-Palmprint-Recognition

(★ 188)

EDCC: An efficient and accurate algorithm for palmprint recognition.

EDCC-Palmprint-Recognition Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# EDCC: An efficient and accurate algorithm for palmprint-recognition

[![Build Status](https://travis-ci.org/Leosocy/EDCC-Palmprint-Recognition.svg?branch=master)](https://travis-ci.org/Leosocy/EDCC-Palmprint-Recognition)
[![codecov](https://codecov.io/gh/Leosocy/EDCC-Palmprint-Recognition/branch/master/graph/badge.svg)](https://codecov.io/gh/Leosocy/EDCC-Palmprint-Recognition)
[![MIT licensed](https://img.shields.io/badge/license-MIT-green.svg)](https://raw.githubusercontent.com/Leosocy/EDCC-Palmprint-Recognition/master/LICENSE)

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

Use the `EDCC` algorithm with [default config](https://github.com/Leosocy/EDCC-Palmprint-Recognition/blob/master/pypackage/edcc/config.py#L18) 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.**
1. **Faster recognition speed.**
1. **Higher recognition accuracy.**

[**More details about `EDCC`**](https://blog.leosocy.top/posts/4354/)

## Installation

### Install library

There are ***some requirements*** if you want to install `EDCC` library:

- OS *nix like.
- [`OpenCV 3.4`](https://docs.opencv.org/3.4/d7/d9f/tutorial_linux_install.html) or [`OpenCV 4.5`](https://docs.opencv.org/4.5.1/d7/d9f/tutorial_linux_install.html) 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 .. && sudo make install`

### Install Python Package

Please make sure that the ***edcc library has been successfully installed*** by following the steps above.

**Python3.x** required.

*Steps:*

1. `cd pypackage`
2. `python setup.py install`

## QuickStart

The project provides a Docker container runtime environment with edcc library and python package installed.

You can quick start accord to the following commands:

```shell
# bootstrap a docker container with edcc library installed
./manage.sh env

# run c example
cd /app/examples/c_example && mkdir -p build && cd build && cmake .. && make && ./run_c_sample

# run cpp example
cd /app/examples/cpp_example && mkdir -p build && cd build && cmake .. && make && ./run_cpp_sample

# run python example
cd /app/examples/py_example && python example.py
```

## Usage

Make sure you have installed [library](#install-library) and [Python package](#install-python-package) before using edcc.

And you can see more usage details under [examples](./examples) directory about usage.

### C/C++

In your CMakeLists.txt, add these lines:

```cmake
find_package(edcc REQUIRED)
include_directories(${EDCC_INCLUDE_DIR})
...
add_dependencies(${YOUR_PROJECT} ${EDCC_LIBRARIES})
target_link_libraries(${YOUR_PROJECT} ${EDCC_LIBRARIES})
```

Then you can use it in your source code(C or C++) like this:

```c
#include <edcc/c_api.h>

#define ASSERT_STATUS_OK(s) \
  do {                      \
    if (s[0] != '\0') {     \
      perror(s + 1);        \
      return -1;            \
    }                       \
  } while (0)

int main() {
  // create a new encoder.
  char status[128];
  int encoder_id = new_encoder_with_config(29, 5, 5, 10, status);
  ASSERT_STATUS_OK(status);
  // encode palmprints to code buffer.
  unsigned long buffer_size = get_size_of_code_buffer_required(encoder_id);
  char* code_buffer_one = (char*)malloc(buffer_size);
  char* code_buffer_another = (char*)malloc(buffer_size);
  encode_palmprint_using_file(encoder_id, one_image_file_path, code_buffer_one, buffer_size, status);
  ASSERT_STATUS_OK(status);
  encode_palmprint_using_file(encoder_id, another_image_file_path, code_buffer_another, buffer_size, status);
  ASSERT_STATUS_OK(status);
  // calculate the similarity score of two codes.
  double score = calculate_codes_similarity(code_buffer_one, code_buffer_another, status);
  ASSERT_STATUS_OK(status);
  return 0;
}
```

C++

```c++
#include <edcc/facade.h>
#include <edcc/status.h>

#define ASSERT_STATUS_OK(s) \
  do {                      \
    if (!s.IsOk()) {        \
      perror(s.msg());      \
      return -1;            \
    }                       \
  } while (0)

using edcc::EdccFacade;
using edcc::Status;

int main() {
  Status s;
  // create a new encoder.
  auto inst = EdccFacade::Instance();
  auto encoder_id = inst->NewEncoderWithConfig(29, 5, 5, 10, &s);
  ASSERT_STATUS_OK(s);
  // encode palmprints to code buffer.
  size_t buffer_size = inst->GetSizeOfCodeBufferRequired(encoder_id);
  char* code_buffer_one = new char[buffer_size];
  char* code_buffer_another = new char[buffer_size];
  inst->EncodePalmprint(encoder_id, one_image_file_path, code_buffer_one, buffer_size, &s);
  ASSERT_STATUS_OK(s);
  inst->EncodePalmprint(encoder_id, another_image_file_path, code_buffer_another, buffer_size, &s);
  ASSERT_STATUS_OK(s);
  // calculate the similarity score of two codes.
  double score = inst->CalcCodeSimilarity(code_buffer_one, code_buffer_another, &s);
  ASSERT_STATUS_OK(s);
  return 0;
}
```

### Python

```Python
import edcc

config  = edcc.EncoderConfig(29, 5, 5 ,10)
encoder = edcc.create_encoder(config)
one_palmprint_code = encoder.encode_using_filename("./palmprint_one.bmp")
another_palmprint_code = encoder.encode_using_filename("./palmprint_another.bmp")
similarity_score = one_palmprint_code.compare_to(another_palmprint_code)
```

## Contributing

Please see [CONTRIBUTING.md](./CONTRIBUTING.md)

EDCC-Palmprint-Recognition

EDCC-Palmprint-Recognition Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

EDCC-Palmprint-Recognition

EDCC-Palmprint-Recognition Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README