Python face recognition attendance system dlib+OpenCV and Pyqt5, database sqlite face recognition system computer graduation design source code

1. Project introduction

Python language, dlib, OpenCV, Pyqt5 interface design, sqlite3 database

This system uses dlib as a face recognition tool. dlib provides a method to map face image data to a 128-dimensional space vector. If two images come from the same person, the distance between the space vectors mapped by the two images will be very close. , otherwise it will be far away. Therefore, it is possible to determine whether they are the same person by extracting pictures and mapping them to a 128-dimensional space vector and then measuring whether their Euclidean distance is small enough.

Method implementation, implementation steps

1. Instantiate face detection model, face key point detection model, face recognition model

2. The computer camera device loads a pair of pictures

3. Obtain the space vectors mapped by the face pictures in the picture, that is, the face feature values

4. Calculate the Euclidean distance of the feature vector, and judge whether they are the same person according to the threshold

2. Screenshot of the project

3. Supplementary instructions

Project run:

1. pycharm opens the entire project file

2. Configure the Python local environment

3. Install the following dependent libraries

4. Start and run run.py

Dependent library:

numpy

PyQt5

opencv-python

dlib

Research Status of Face Recognition

The realization of face recognition generally includes two processes of face detection and algorithm recognition. Face detection uses a face detector, and face recognition requires a face recognition algorithm. The following is an introduction to several common face detectors and face recognition algorithms.

Common Face Detectors

The four common face detection methods are OpenCV's Haar face detector, OpenCV face detector based on deep learning, Dlib's HOG face detector and MTCNN face detector. The detailed introduction of each detector is shown in Table 1-1:

Table 1-1 Comparison table of face detectors

Table 1-1 Comparison of face detectors

Name Description Advantages Disadvantages

OpenCVHaar face detection The cascade function of OpenCV's Haar face detection is obtained through training in a large number of positive and negative images 1. Run in real time on CPU

2. Simple structure

3. Can be used to detect faces of different instances

1. Affected by light, the accuracy is not high

2. Not suitable for non-frontal images

3. Does not work under occlusion

OpenCV face detector based on deep learning 

Based on the Single-Shot-Multibox detector, and use the ResNet architecture as the backbone. There are two models of Caffe and Tensorflow

1. The most accurate of the four methods

2. Run in real time on the CPU

3. It is suitable for different face directions, up, down, left, right, side, etc.

4. Can work under severe occlusion

5. Can detect faces of various scales

Not as fast as Dlib HoG's face detector

Dlib HOG+SVM face detection 

Image-based Hog features, integrated support vector machine algorithm implementation

1. The fastest method on the CPU

2. Suitable for frontal and slightly non-frontal faces

3. Can work under small occlusion

1. The main disadvantage is that it cannot detect small faces; 2. The bounding box generally excludes part of the forehead or even part of the chin sometimes; 4. Does not work well under severe occlusion; 5. Not suitable for side and extreme non-frontal, such as looking down or looking down

MTCNN face detection CNN-based feature-based maximum-margin object detector 

1. Suitable for different face orientations

2. Robust in occasional scenarios.

3 is very fast on GPU

1. CPU speed is very slow

2. Does not detect small faces

3. The bounding box is smaller than the HoG detector

Common Face Recognition Algorithms

The most common face recognition algorithms at this stage are: EigenFace eigenface method, FisherFace LDA linear discrimination, LBPH face recognition algorithm (the face recognition of this system uses this algorithm, and the face recognition implementation process of this algorithm is the second Chapter will explain in detail).

(1) EigenFace feature face method

EigenFace eigenface is a face recognition algorithm based on Principal Component Analysis (PCA, Principal Component Analysis) [5], and it is also the first algorithm that applies statistical methods to face recognition research. It is based on the KL transform and applies PAC principal component analysis to face recognition. The idea is to project the original image into the feature space, obtain a lot of dimensionality reduction images, and then represent the main components of the obtained dimensionality reduction images as human faces. Also known as "eigenfaces".

FisherFace LDA linear discrimination is a face recognition algorithm based on LDA (Linear Discriminant Analysis, linear discriminant analysis) [6]. The common point between LDA and PCA is that they both determine the principal components through the method of eigenvalues. The difference is that PCA is the eigenvalue representation of the covariance matrix, and the matrix obtained by LDA is more complex than PCA. There is also a big difference between the two in calculating the average value of the average face. The former is to calculate the average value of each sample data that belongs to the same class judged by the Fisher criterion, and the latter is to directly calculate the average value of all sample data. calculate. The criterion of LDA linear discrimination is to expand the distance between images of different faces and reduce the distance between different images of the same person.

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