Enhanced Face Recognition Method Based On Local Binary Pattern and Principal Component Analysis For Efficient Class Attendance System

2019 ◽  
Author(s):  
Howard Chin ◽  
Kit Hwa Cheah ◽  
Humaira Nisar ◽  
Kim Ho Yeap
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Class Attendance

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

scholarly journals Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition

2020 ◽  
pp. 34-44
Author(s):  
Md. Mahbubul Alam ◽  
Md. Ashikur Rahman Khan ◽  
Zayed Us Salehin ◽  
Main Uddin ◽  
Sultana Jahan Soheli ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Biometric System ◽  
Recognition Method ◽  
Person Identification ◽  
Multimodal Biometric System ◽  
Recognition Efficiency

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

scholarly journals Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

2018 ◽  
Vol 197 ◽  
pp. 03001
Author(s):  
Ichsan Taufik ◽  
Maya Musthopa ◽  
Aldy Rialdy Atmadja ◽  
Muhammad Ali Ramdhani ◽  
Yana Aditia Gerhana ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Recognition Time ◽  
Characteristic Extraction ◽  
Face Recognition System ◽  
Sample Test

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

scholarly journals Comparison of Different Face Recognition Method Based On PCA

2014 ◽  
Vol 10 (4) ◽  
pp. 2016-2022
Author(s):  
Rajib Saha ◽  
Debotosh Bhattacharjee ◽  
Sayan Barman
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Manhattan Distance ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
Face Matching ◽  
Chebychev Distance

This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Eucleadian distance & Chebychev distance for face matching.

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