Web and mobile based facial recognition security system using Eigenfaces algorithm

Rodavia, Maria Rosario D.; Bernaldez, Orlando; Ballita, Maylane

doi:10.1109/tale.2016.7851776

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Rodavia et al [59] implemented a security system based on face recognition for web and mobile devices. The recognition is developed using Eigenfaces.…”

Section: E) Eigenfaces and Fisherfacesmentioning

confidence: 99%

Machine learning models in people detection and identification

2022

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Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020. Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review? Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people. Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria. Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes. Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes. Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people. Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.

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“…Rodavia et al [59] implemented a security system based on face recognition for web and mobile devices. The recognition is developed using Eigenfaces.…”

Section: E) Eigenfaces and Fisherfacesmentioning

confidence: 99%

Machine learning models in people detection and identification

2022

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“…Mobile-based face recognition technology is a rapidly developing field with significant implications for security, law enforcement, and mobile applications (Arisandi et al, 2018;Samet & Tanriverdi, 2017). Researchers are currently investigating the accuracy and performance of mobile-based face recognition systems compared to traditional desktop-based systems (Abuzar et al, 2020;Alburaiki et al, 2021;Rodavia et al, 2017). There is growing concern about the privacy implications of mobile-based face recognition and how it can be used to identify individuals without their consent or knowledge (Ahmed Khan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods

Achmad,

AZ,

Solichin

2023

J. Ris. Inform.

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Using biometric technology for recording attendance in the school environment is still not widely done by researchers. In this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. The solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). The built system can record the presence of students when entering the classroom and when returning home or out of class. Proposed methods for identifying student attendance are the Haar Cascade and Eigenface algorithms. The system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. Based on the test results, the method can provide accurate and fast facial recognition results. The presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity.

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“…The face feature problem has been noticed by some recent works in which an efficient and effective image representation has been proposed by using local and holistic features. The Eigenface [20,21], Randomface [22], and Fisherface [21] are all classical holistic features [23], but some other works argued that those holistic features can be easily affected by variables such as illumination, pose, facial expression, and noise. Therefore, they introduced some local features such as LBP [24] and Gabor filter [25,26].…”

Section: Introductionmentioning

confidence: 99%

Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

Liu

et al. 2018

Mathematical Problems in Engineering

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In recent years, sparse representation based classification (SRC) has emerged as a popular technique in face recognition. Traditional SRC focuses on the role of the 1 -norm but ignores the impact of collaborative representation (CR), which employs all the training examples over all the classes to represent a test sample. Due to issues like expression, illumination, pose, and small sample size, face recognition still remains as a challenging problem. In this paper, we proposed a patch based collaborative representation method for face recognition via Gabor feature and measurement matrix. Using patch based collaborative representation, this method can solve the problem of the lack of accuracy for the linear representation of the small sample size. Compared with holistic features, the multiscale and multidirection Gabor feature shows more robustness. The usage of measurement matrix can reduce large data volume caused by Gabor feature. The experimental results on several popular face databases including Extended Yale B, CMU PIE, and LFW indicated that the proposed method is more competitive in robustness and accuracy than conventional SR and CR based methods.

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Web and mobile based facial recognition security system using Eigenfaces algorithm

Cited by 8 publications

References 8 publications

Machine learning models in people detection and identification

Machine learning models in people detection and identification

Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods

Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

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