Face recognition system based on principal components analysis and distance measures

Goyal, Aarti; Meenpal, Ankita

doi:10.14419/ijet.v7i2.21.11826

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…This algorithm uses different distance measures as matching technology and uses principal component analysis for face feature extraction and data representation. The feature dimension of the generated face image is low [ 4 ]. Naji and Hamd believed that different databases and methods should be used to identify people and proposed to use the local binary mode and ternary mode in the texture analysis method for comparative analysis.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Face Recognition System Based on Universal Design Concept

Shin

2022

Computational Intelligence and Neuroscience

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The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people’s life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users’ physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.

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Section: Related Workmentioning

confidence: 99%

Intelligent Face Recognition System Based on Universal Design Concept

Shin

2022

Computational Intelligence and Neuroscience

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“…Further validate the effectiveness of teaching quality evaluation in the hybrid teaching mode of this article's method, calculate the correlation degree of the quality evaluation model using formula (17), and compare the method of reference [7], the method of reference [8], and this article's method. The correlation degree of the quality evaluation models for different methods is shown in Table 1.…”

Section: Figure 2 Coverage Of Quality Evaluation Indicators By Differ...mentioning

confidence: 87%

“…Due to the numerous indicators involved in the evaluation of teaching quality in the blended teaching mode, there is usually a significant correlation between the indicators. Using principal component analysis [17][18][19], the selected indicators are simplified to comprehensive indicators with relatively small correlation, and the principal components of the teaching quality evaluation indicators in the blended teaching mode are obtained.…”

Section: Construction Of Teaching Quality Evaluation Model Under Hybr...mentioning

confidence: 99%

“…1, ≤ 0.2 In formula (17), represents the correlation function, ε represents the random variables in the transaction data, and represents the number of indicators involved in processing. The higher the correlation of the quality evaluation model, the better the teaching quality evaluation effect under the mixed teaching mode of the method.…”

Section: Selection and Calculation Of Performance Indicatorsmentioning

confidence: 99%

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Unveiling Hybrid Teaching Efficacy: An Association Rule-Based Approach to Evaluating Quality

Chourasiya,

Dwivedi

2024

Preprint

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This paper presents a teaching quality evaluation method based on association rules, using the Analytic Hierarchy Process (AHP) and the Apriori algorithm to improve the effectiveness of the evaluation results of teaching quality in blended learning mode. The experimental results show that this method has a good teaching quality evaluation effect in the mixed teaching mode, with a high average coverage rate of quality evaluation indicators and correlation degree of quality evaluation model. These experimental results imply that the method employed in the study is not only thorough in evaluating various quality indicators but also efficient in terms of processing time. However, it's important to consider the specific indicators and methodologies used in the study, as well as the context in which the evaluation was conducted, to fully understand the implications and generalizability of the findings.

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“…It is important technique to remove irrelevant or redundant features [26], [27]. PCA plays a crucial role in various fields like: Artificial intelligence, biometrics etc [27]. Mathematically it can be stated as When n observations of object Y are given in the d-dimensional space:…”

Section: Pca (Principle Component Analysis)mentioning

confidence: 99%

An efficient technique for hybrid classification and feature extraction using normalization

Kaur¹,

Bathla²

2018

IJET

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Text classification is technique for assigning the class or label to a particular document within predefined class labels. Predefined classes examples are sports, business, technical, education and science etc. Classification is supervised learning technique i.e. these classes are trained with certain features and then document is classified based on similarity measure with these trained document set. Text classification is used in many applications like assigning the label to the documents, separating the spam messages from the genuine one, filtering of text, natural language processing etc. Feature selection, extraction and classification are various phases for assigning label to any document. In this paper, PCA is used for feature extraction, ABC is used for feature selection and SVM is used for classification. PCA is improved by applying normalization-using size of features in our proposed approach. It reduces the redundant features to larger extent. There are very few research works, which have implemented PCA, ABC and SVM for complete classification. Evaluation parameters like accuracy, F-measure and G-mean are calculated to check classifier efficiency. The proposed system is deployed on 20-Newsgroup dataset. Experiment analysis proves that accuracy is improved using our proposed approach as compared to existing approaches.

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Face recognition system based on principal components analysis and distance measures

Cited by 6 publications

References 19 publications

Intelligent Face Recognition System Based on Universal Design Concept

Intelligent Face Recognition System Based on Universal Design Concept

Unveiling Hybrid Teaching Efficacy: An Association Rule-Based Approach to Evaluating Quality

An efficient technique for hybrid classification and feature extraction using normalization

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