Proposed approach to improve facial recognition techniques for occluded faces by Covid-19 mask protection

Ennaama, Faouzia; Benhida, Khalid; ESSALKI, S.

doi:10.1016/j.ifacol.2022.07.354

Cited by 2 publications

(1 citation statement)

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“…The eigenface method, as described by Ennaama and referenced from Turk, is among the earliest effective techniques for facial identification (Ennaama et al, 2022) The Eigenfaces approach identifies distinctive facial attributes and expresses them as a linear combination of eigenfaces derived from the feature extraction procedure (Rudraraju et al, 2020, p. 240).…”

Section: The Eigenface Methodsmentioning

confidence: 99%

Application Of the Eigenface Method in The Presence System Using Web-Based Face Recognition

Darmawan,

Kusnandar,

Fauka

et al. 2024

imeisj

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The swift progress of technology has yielded numerous advantages, particularly for organizations engaged in vocational education, such as E-learning Marketplace in Bandung (Luarsekolah). One of the advantages of technology in vocational education is the use of attendance system technology. Luarsekolah's attendance system employs a conventional fingerprint recognition method, which frequently encounters difficulties when employees attempt to clock in, as their fingerprints are occasionally not acknowledged. Hence, creating a system that facilitates and streamlines employee attendance without requiring physical interaction is imperative. To address this, a facial recognition system utilizing the eigenface approach, with a development methodology based on the waterfall method, is suggested. Using the Eigenface PCA (Principal Component Analysis) technique, this system can function in real time. The research findings demonstrate the efficacy of the eigenface approach in real-time applications. The eigenface approach incorporates multiple validations to ensure the system's accuracy when employees utilize it. The approach incorporates several validations, such as presenting a message when an employee's face is near the camera and indicating a message when the employee's face is not directed toward the camera. If the camera misses adequate lighting, the system will automatically modify the lighting conditions to enable the camera to capture the employee's facial features.

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Section: The Eigenface Methodsmentioning

confidence: 99%