Traffic Violation Detection using Principal Component Analysis and Viola Jones Algorithms

This paper describes an application to detect traffic rule violation using principal component analysis algorithm(PCA).The proposed system will detect crowded bikes using PCA and Viola Johnson algorithms. The viola-Jones computation is seen as convincing in order to check and focus the face features. The face acknowledgment is strategy of perceiving region of face from a picture of one or different individuals together. The perceived face is removed in the proposed using the viola-Jones estimation. This application uses camera to recognize the amount of faces in the edge which identifies with number of people going in a bike. As indicated by the organization controls only two adults or two adults and one adolescent are permitted to go in a bike. We use Violo Johnes and PCA Algorithm to perceive the appearances to choose the amount of faces in the edge. Consequently the endeavor derives that through this structure we execute OCR to check the number plate to recognize the bike liberating with numerous people. This is a customized system to keep up a vital good ways from the accident by driving past the limited part on bike. At the point when our system perceives the over-trouble vehicle, the number plate of the vehicles is discovered using OCR.

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MATLAB Implementation of Face Identification Using Principal Component Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.5402 ◽

2012 ◽

Vol 433-440 ◽

pp. 5402-5408

Author(s):

Nasrul Humaimi Mahmood ◽

Ismail Ariffin ◽

Camallil Omar ◽

Nur Sufiah Jaafar

Keyword(s):

Principal Component Analysis ◽

Minimum Distance ◽

Principal Component ◽

Component Analysis ◽

Face Identification ◽

Human Face ◽

Face Images ◽

The Face ◽

Matlab Implementation ◽

Pca Algorithm

Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (PCA) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the PCA algorithm for human face identification through a webcam.

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Half-face based recognition using principal component analysis

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i3.pp1404-1410 ◽

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>

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A subspace type incremental two-dimensional principal component analysis algorithm

Journal of Algorithms & Computational Technology ◽

10.1177/1748302620973531 ◽

2020 ◽

Vol 14 ◽

pp. 174830262097353

Author(s):

Xiaowei Zhang ◽

Zhongming Teng

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Streaming Data ◽

High Dimensional ◽

Two Dimensional ◽

Incremental Updating ◽

Principal Component Analysis Algorithm ◽

Pca Algorithm ◽

Incremental Pca

Principal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incremental updating of the eigenspace to compute several principal eigenvectors at the same time for the online feature extraction. The algorithm overcomes the problem that the approximate eigenvectors extracted from the traditional incremental two-dimensional PCA algorithm (I2DPCA) are not mutually orthogonal, and it presents more efficiently. In numerical experiments, we compare the proposed SI2DPCA with the traditional I2DPCA in terms of the accuracy of computed approximations, orthogonality errors, and execution time based on widely used datasets, such as FERET, Yale, ORL, and so on, to confirm the superiority of SI2DPCA.

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