Rancang Bangun Aplikasi Pengenalan Wajah menggunakan Metode Viola-Jones dan Algoritma PCA

Yuliana, Yuliana; Nurhaida, Ida

doi:10.22441/incomtech.v8i3.3385

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Principal Component Analysis (PCA) adalah salah satu metode yang populer digunakan untuk mereduksi dimensi citra (Vidal et al, 2018) (Rahmawan, 2020) (Andrian, 2019) dari ruang citra sehingga basis baru dapat menggambarkan model yang khas/unik dari citra tersebut (Yuliana & Nurhaida, 2018). PCA diciptakan pada 1901 oleh Karl Pearson (Vidal et al, 2018)…”

Section: Pca (Principal Component Analysis)unclassified

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Rambe

Tanjung

Muhathir

2022

JITE

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Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock < Euclidian < Minkowski < Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better.

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Section: Pca (Principal Component Analysis)unclassified

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Rambe

Tanjung

Muhathir

2022

JITE

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“…This method has been proven in the performance test containing mathematical calculation [10], yet no face recognition system on a low-spec computer system has applied this algorithm. In its implementation, previous research has employed this method on the face recognition system for students' attendance applications [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Face Recognition Using Geometric Features Extraction

Hidayat,

Wibowo,

Satria

et al. 2022

kursor

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The face is among the biometric objects used to recognize oneâ€™s identity. There are various face recognition system methods that can be applied, one of which is geometric features-based face recognition. Geometric features are unique features extraction of oneâ€™s facial components. These features are obtained by calculating the comparison values of the distance measurement between facial components served as a reference like eyes, nose, and mouth. This research implemented a face recognition system using the geometric features method on a significantly low-spec computer system. This implementation was carried out by building a system, installing it on a computer system, and then testing it using laptops or computer devices and the camera web. The face recognition system would process the facial input images, extract their geometric features, and match the results with the data stored in the database. The research results were a low-spec computer system that could recognize its users by providing real-time feedback in the form of usersâ€™ names with an accuracy of 98%.

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The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN

2021

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The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.

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Rancang Bangun Aplikasi Pengenalan Wajah menggunakan Metode Viola-Jones dan Algoritma PCA

Cited by 3 publications

References 9 publications

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Implementation of Face Recognition Using Geometric Features Extraction

The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN

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