1、指纹识别数据集；

FVC2002 - Second International Fingerprint Verification Competition

2、数据预处理

1）贴标签分类；

将多种指纹放在多个文件夹里面，将文件夹备注不同的指纹名称；

3、编写matlab代码&配置参数

基于Alexnet框架编写代码（出于保密需求，代码省略，感兴趣的朋友可以留言讨论）

4、深度学习

对分类好的数据集进行深度学习；

5、测试

用20%的数据进行测试，判断预测准确性。

代码如下：

Here's some MATLAB code to train and test a convolutional neural network (CNN) model with LeNet-5 architecture, which is one of the most widely used CNN architectures in computer vision. The training data consists of 2048x196 images that are cropped from larger images using OpenCV library for image processing.
```m file:
function [net_params] = lecnn(im) % LeNet-5 architecture with Alexnet pretrained weights
    clear all; close all; clf;

% Load the CNN model parameters and initialize variables
load('leclas.mat'); load('alexnet128.mat') ;

% Define input image dimensions (width, height) for training data
im_dim = [640 594]; % Image size in pixels of training images

% Load the AlexNet pretrained weights and initialize variables with them
load('alexnet128.mat') ; clear all; close all; clf;

% Define input image dimensions (width, height) for testing data
im_dim = [640 594]; % Image size in pixels of test images

% Initialize variables with the AlexNet pretrained weights and parameters
net.params(:)=alexnet128; net.weights=leclas; clear all; close all; clf ;

% Define input image dimensions (width, height) for training data
im_dim = [640 594]; % Image size in pixels of training images

% Load the CNN model parameters and initialize variables with them
load('leclas.mat'); clear all; close all; clf ;

% Define input image dimensions (width, height) for testing data
im_dim = [640 594]; % Image size in pixels of test images

% Initialize variables with the LeNet-5 parameters and weights
net.params(:)=leclas; net.weights=leclas ; clear all; close all; clf ;