A Study about Principle Component Analysis and Eigenface for Facial Extraction

Erwin, Erwin; Azriansyah, Muhammad; Hartuti, N; Fachrurrozi, Muhammad; Tama, Bayu Adhi

doi:10.1088/1742-6596/1196/1/012010

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…"The tricks that make you invisible to computers make you a remarkable sight for people around you," says Robinson Meier of The Atlantic. [4], [7], [16] II. THE PROPOSED APPROACH The proposed approach goes through the following steps as shown in figure 1 Step 1: Detect human behavior and weapon visibility to define if the behavior is either normal or abnormal [11], [42].…”

Section: Deceive Surveillance Systemsmentioning

confidence: 99%

Crime Detection and Suspect Identification System

Hosameldeen¹,

Alhammadi²,

SALEHI³

2023

Preprint

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—The number of ATMs in various countries is increasing steadily and rapidly with the number of users increasing very widely. On the other hand, banks have become more interested in finding the best procedures to combat ATM crimes to ensure the safety and security of their customers and other cardholders. This has become an excellent target for some criminals or fraudsters, despite the limited amounts that can be withdrawn from these devices, given a maximum daily limit. We aim at implementing this system inside bank ATMs in order to detect objects like guns, hammers, and knives. Once the suspicious objects and actions are detected, we perform facial recognition to identify whether the suspect is a repeating offender. We use object, face, and action recognition algorithms to achieve our objective. Results showed that using our proposed algorithm is efficient in detecting threatening objects

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Section: Deceive Surveillance Systemsmentioning

confidence: 99%

Crime Detection and Suspect Identification System

Hosameldeen¹,

Alhammadi²,

SALEHI³

2023

Preprint

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show abstract

“…A survey on existing facial recognition methods in security door lock system has been investigated. Based on [7], face detection, face alignment, feature extraction, and recognition procedure are the four main components of a face recognition system. For instance, face detection is applied to detect faces and convert into vector from a digital image.…”

Section: Related Workmentioning

confidence: 99%

SAFE: Security Door Lock System Using Haar-Cascade and LBPH Method

Leim

Arpitha

et al. 2023

ACE

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Door lock provides numerous benefits and has become indispensable in daily life as it acts as a security guard to prevent burglars and protect home belonging safely. The unlock methods of existing door lock system widely use keys and thumbprint involve touching the object may cause the spread of COVID-19. In this paper, a computer vision based security door lock system using Raspberry Pi (called SAFE) is proposed. Haar-Cascade classifier is employed as face detection classifier, while Local Binary Pattern Histogram (LBPH) is proposed as face recognition classifier. Recognition result is processed based on the usage of user to provide insights of SAFE. The accuracy of SAFE using pre-trained LBPH classifier achieves average of 86% based on the data obtained. The recognition speed outperforms existing work using principal component analysis and eigenfaces.

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“…To solve the problem of ICP falling into local optimal solution, the most effective method is to divide the registration process into two stages: rough registration and fine registration. The feature-based registration method is used to extract points, 27 lines, 28 and surfaces 29 for rough registration, providing good initial pose for subsequent ICP fine registration. This method effectively avoids the algorithm falling into local convergence.…”

Section: Introductionmentioning

confidence: 99%

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Zhao,

Zhang,

et al. 2023

Measurement and Control

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For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.

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A Study about Principle Component Analysis and Eigenface for Facial Extraction

Cited by 15 publications

References 8 publications

Crime Detection and Suspect Identification System

Crime Detection and Suspect Identification System

SAFE: Security Door Lock System Using Haar-Cascade and LBPH Method

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

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