Implementasi Pengenalan Wajah Menggunakan PCA (Principal Component Analysis)

https://doi.org/10.22146/ijeis.3892

Dian Esti Pratiwi(1*), Agus Harjoko(2)

(1) 
(2) Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Abstrak

Sistem identifikasi berkembang dengan cepat. Perkembangan tersebut mendorong kemajuan sistem keamanan berbasis biometrik. Pengenalan wajah adalah  salah satu sistem identifikasi yang dikembangkan berdasarkan perbedaan ciri wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi.

Eigenface merupakan salah satu metode pengenalan wajah berdasarkan Principal Component Analysis (PCA) yang mudah diimplementasikan. Eigenface dimulai dengan pemrosesan awal untuk mendapatkan hasil citra yang lebih baik. Setelah itu menghitung eigenvector dan eigenvalue dari citra wajah untuk dilakukan proses training image. Proses training wajah yaitu mencari eigenvector, eigenvalue dan average image yang  diproyeksikan ke dalam subruang PCA. Proyeksi ke dalam subruang PCA digunakan untuk menyederhanakan data citra yang tersimpan. Perbandingan terkecil proyeksi PCA antara file database dan input menentukan hasil nama pengguna. Perbandingan nilai terkecil dicari menggunakan Nearest Neighbor.

 Program pengenalan wajah menampilkan salah satu nama pengguna yang telah tersimpan dalam database. Pengujian menggunakan ekspresi senyum dan tanpa ekspresi pada delapan orang dan 16 wajah. Prosentase keberhasilan proses pengenalan wajah adalah 82,81%. Beberapa faktor yang mempengaruhi keberhasilan pengenalan yaitu pencahayaan pada wajah, jarak wajah dengan webcam, banyaknya gambar wajah orang yang tersimpan dan performa komputer yang digunakan.

 

Kata kunci Eigenface, eigenvector, eigenvalue, average image

 

Abstract

 Identification system grow quickly. The  development encourage security system progress based biometric. Face recognition is one of the identification system is developed based on different characteristic of a person’s face based biometric which has a high accuracy.

Eigenface is one method of face recognition based on PCA (Principal Component Analysis) which easy to implement. It begin with initial processing to get a better image. Then computing  eigenvector and eigenvalue from face image for further training image process. Training process is finding the eigenvector, eigenvalue and average image to be projected into the PCA subspace. Projection into the PCA subspace is used to simplify the image data stored. The smallest PCA projection comparison between the database and the input file is determinants the result of username. Smallest value comparison searched using Nearest Neighbor.

 Face recognition program show one of username that has been stored in a database. The test using smile expression and without expression in eight people and 16 faces. The percentage of successful face recognition process is 82,81%.  Several factors that influence the success of the recognition are lighting on the face, the face distance with a webcam, sum face image of people saved and used computer performance.

 

Keywords Eigenface, eigenvector, eigenvalue, average image


Keywords


Eigenface, eigenvector, eigenvalue, average image

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References

[1] Shervin, 2010, Introduction to Face Detection and Face Recognition, http://www.shervinemami.info/faceRecognition.html diakses tanggal 23 Mei 2012 jam 9.29

[2] Lyon, Douglash., Vincent. N. 2009. Interactive Embedded Face Recognition. Object Technology 8:23-25

[3] Munir. R. 2004. Pengolahan Citra Digital Dengan Pendekatan Algoritmik. Informatika. Bandung

[4] Lim, Resmana., Raymond,. Kartika. G. 2002. Face Recognition Menggunakan Metode. Linear Discriminant Analysis (LDA). Proceding Komputer dan Sistem Intelijen. Jakarta 21-22 Agustus 2002



DOI: https://doi.org/10.22146/ijeis.3892

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