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An Analysis on Face Recognition using Principal Component Analysis Approach

Authors:

Burra Venkata Durga Kumar,

Kelvin J.A Ooi,

Iftekhar Salam,

Mohammad Arif Sobhan BhuiyanAuthors Info & Claims

ICIGP '22: Proceedings of the 2022 5th International Conference on Image and Graphics Processing

Pages 150 - 158

https://doi.org/10.1145/3512388.3512411

Published: 28 March 2022 Publication History

Abstract

Face recognition is a type of biometric recognition based on human facial feature information. Human face images or videos can be automatically collected using a high definition camera. Advanced technologies then can be used for face recognition by tracking on collected images to detect human faces. The facial recognition algorithm can cut out the main facial area after detecting the face and find the key facial feature points, and input it into the recognition algorithm after processing. To extract and compare the facial features, the recognition algorithm is used to the complete the final classification. This research is to study the face recognition using PCA (Principal Component Analysis. The PCA-based is used to eigenface recognition, Hotelling transform in PCA is used to obtain the main components of the face distribution, i.e. the feature vectors (eigenfaces). The face images from the training library and the face images to be recognized are projected onto this space separately to match the recognized image output based on the principle of minimum geometric distance. The face angle, face mask, and face expression factors are selected for testing against face recognition. Hence questions and hypotheses are formulated to verify whether the recognition rate of face recognition is influenced by these factors for this recognition method. Based on the results of the analysis it was that face angle, face masking have a positive effect on face recognition. Furthermore, according to the analysis, it can be concluded that face masking has the highest significance for face recognition.

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Cited By

Ho HVuong Nguyen LHuong Thi Le TLee O(2024)Face Detection Using Eigenfaces: A Comprehensive ReviewIEEE Access10.1109/ACCESS.2024.343596412(118406-118426)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3435964
Saheel SAlvi AAni AAhmed TUddin M(2024)Semi-supervised, Neural Network based approaches to face mask and anomaly detection in surveillance networksJournal of Network and Computer Applications10.1016/j.jnca.2023.103786222:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jnca.2023.103786
Zayer MAbbas E(2023)Improvement of Face Recognition Rate under Different Illumination by Using Exposure Fusion for Greyscale Images2023 International Conference on Engineering Applied and Nano Sciences (ICEANS)10.1109/ICEANS58413.2023.10630461(89-95)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICEANS58413.2023.10630461

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cover image ACM Other conferences

ICIGP '22: Proceedings of the 2022 5th International Conference on Image and Graphics Processing

January 2022

391 pages

ISBN:9781450395465

DOI:10.1145/3512388

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 March 2022

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Xiamen University Malaysia

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ICIGP 2022

ICIGP 2022: 2022 the 5th International Conference on Image and Graphics Processing

January 7 - 9, 2022

Beijing, China

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Cited By

Ho HVuong Nguyen LHuong Thi Le TLee O(2024)Face Detection Using Eigenfaces: A Comprehensive ReviewIEEE Access10.1109/ACCESS.2024.343596412(118406-118426)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3435964
Saheel SAlvi AAni AAhmed TUddin M(2024)Semi-supervised, Neural Network based approaches to face mask and anomaly detection in surveillance networksJournal of Network and Computer Applications10.1016/j.jnca.2023.103786222:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jnca.2023.103786
Zayer MAbbas E(2023)Improvement of Face Recognition Rate under Different Illumination by Using Exposure Fusion for Greyscale Images2023 International Conference on Engineering Applied and Nano Sciences (ICEANS)10.1109/ICEANS58413.2023.10630461(89-95)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICEANS58413.2023.10630461

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