Automated Class Attendance System based on Face Recognition using PCA Algorithm

2015 ◽  
Vol V4 (12) ◽  
Author(s):  
D. Nithya ◽  
Keyword(s):  
Face Recognition ◽  
Class Attendance ◽  
Pca Algorithm

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

2016 ◽  
Vol 30 (08) ◽  
pp. 1656005 ◽  
Author(s):  
Jiadi Li ◽  
Zhenxue Chen ◽  
Chengyun Liu
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Low Resolution ◽  
Multi Scale ◽  
Face Features ◽  
Weighted Principal Component ◽  
Pca Algorithm

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

The Application of Face Recognition Based on OpenCV

2011 ◽  
Vol 403-408 ◽  
pp. 2350-2353
Author(s):  
Su Li
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Biometric Recognition ◽  
The Core ◽  
Face Recognition System ◽  
Learning Functions ◽  
Key Technologies ◽  
Pca Algorithm ◽  
Mathematical Operations

Face recognition is a significant method, which is one of the biometric recognition. A face recognition system consists of two key technologies, namely, face detection and face recognition. In order to achieve two key technologies, Haar-Like feature and AdsBoost algorithm can be used to achieve face detection module. And PCA algorithm can be used to achieve face recognition module. For achieve application more quickly and efficiently, the core of the system develops with OpenCV. And the main use is its image processing, mathematical operations, and machine learning functions.

scholarly journals Face Recognition Based Attendance Record System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1610-1612
Author(s):  
Prof. C. S. More
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Security System ◽  
Fingerprint Recognition ◽  
Recognition Method ◽  
Record System ◽  
Class Attendance

In today’s world, face recognition is the type of biometric that is used in almost every field. This technology is used for security purposes and can be used in many verification and security system. Though it is less efficient than eyes recognition and fingerprint recognition, is still in market due to its untouchability and non-intrusive method. Besides, face recognition should also be utilized for attendance checking in schools, colleges, offices, etc. Face Recognition method pivot to build up a class attendance system which uses the idea of face recognition as present hand done attendance process is lethargic and not suitable to keep. And there are chances of too much proxy attendance. Thus, the want for this method is much needed. This method involves 4 stages- database introduction, face detection, face recognition, attendance updation. The database is made by taking the snap shots of the students in elegance. Face detection and popularity is done using python opencv. Attendance is to be exported at the end of semester.

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