# Face Recognition with KNN in MATLAB
**Abstract**-This paper mainly focus the recognize a person’s identity is important mainly for security reason, but it could also be used to obtain quick access to medical, criminal, or any type of records. Solving this problem is important because it can protect everyone's personal information from being leaked.
**Index Terms**— MATLAB, Image Processing, Eigen Vector, Eigen Values, Euclidean Distance. Signal System, PCA, Image Recognition, Image Segmentation, KNN
### 1.INTRODUCTION
The idea for this paper came up while through gate of the campus and FaceID in Macbook or smartphone, mainly paper is based upon MATLAB comprising image processing. I had read different algorithms based on image processing like Adam’s algorithm, fisher face algorithm, Back Propagation Neural Network etc. But I will show the simplest way of implementing a face recognition system using MATLAB , Here no machine learning or Convolutional neural network (CNN) is required to recognize the faces. To keep the face recognition system as simple as possible, I used eigenvector based recognition system.
### 2.REQUIREMENTS
SYSTEM: CentOS 7.5.1804 Core (RHEL 7.x)
Kernel: x86_64 Linux 3.10.0-862.el7.x86_64
Shell: bash 4.2.46
CPU: Intel Core2 Duo P8600 @ 2x
2.401GHz
RAM: 2358MiB / 3847MiB
GPU: Mesa DRI Mobile Intel® GM45
Express Chipset
SOFTWARE: MATLAB R2018a
3.PROPOSED DESIGN OF MODEL
### 3.PROPOSED DESIGN OF MODEL
![Model.png](picture/Model.png)
### 4.IMPLEMENTATION
![IMPLEMENTATION.png](picture/IMPLEMENTATION.png)
### 5.RECOGNITION PROCEDURE
A face image is transformed into its eigen image components. Now according KNN ,we can realize face recognition.
### 6.MATLAB CODING
[Face_Recognition.m](coding/Face_Recognition.m)
[load_database.m](coding/load_database.m)
### 7.TEXT RESULT
![RESULT.png](picture/RESULT.png)
### 8.LIMITATION
- 1.Large amount of calculation
- 2.Sample imbalance problem
- 3.Consume a lot memory
### 9.APPLICATION
- 1.FaceID in PC and phone
- 2.Door lock for person
### 10.CONCLUSION
First we input a image and observed the Euclidean distance that can tells us how close the input image from the image on set. Then according that we know the face whether know.
### 11.REFERENCES
[[1] International Journal of Scientific & Engineering Research, Volume 3, Issue 2, February 2012 5 ISSN 2229-5518](REFERENCES)
[[2]MIT University media laboratory, U](REFERENCES)
[[3]Olivetti Research Laboratory, UK](REFERENCES)
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温馨提示
本次实验尝试通过将人脸的图像转化为特征向量,然后训练数据集,通过计算欧拉距离找到与待测人脸最接近的k个人脸,这样对人脸进行归类识别实现一个基于KNN 的人脸识别算法,来达到人脸识别的入门级学习。 算法简介: KNN算法假设给定一个训练数据集,其中的实例类别已定。分类时,对新的实例,根据其 k 个最近邻的训练实例的类别,通过多数表决等方式进行预测。因此,KNN算法不具有显式的学习过程。 KNN算法实际上利用训练数据集对特征向量空间进行划分,并作为其分类的“模型”。 k值的选择、距离度量以及分类决策规则是k近邻算法的三个基本要素。 算法流程: 1.假设有一个带有标签的样本数据集(训练样本集),其中包含每条数据与所属分类的对应关系。遍历训练数据集,计算预测样本与其他每一个样本点的距离,按照由近到远排序。完成训练得到训练后的数据集After training Data Set 2.定义一个KNN参数k 值(1<=k<=20),表示纳入投票决策的样本数 3.输入没有标签的新数据后,将新数据的每个特征与样本集中数据对应的特征进行比较(进行测试集的测试)。 4.取前 k个样本数据对应的分。内容来源于
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(175881858)基于KNN算法的MATLAB人脸识别-课程设计.zip (418个子文件)
Face Recognition with KNN in MATLAB.doc 829KB
Face Recognition with KNN in MATLAB(YangBingxue).docx 183KB
Face Recognition with KNN in MATLAB.docx 157KB
flowchart.docx 38KB
.~ce Recognition with KNN in MATLAB.docx 162B
Face_Recognition.m 1KB
load_database.m 699B
README.md 2KB
82206249.pdf 1.14MB
Learning representations by back-propagating errors.pdf 475KB
Analysis-of-Face-Recognition-in-MATLAB.pdf 373KB
Face Recognition with KNN in MATLAB(YangBingxue).pdf 356KB
Face Recognition with KNN in MATLAB.pdf 219KB
perceptrons-2017.pdf 164KB
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