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Abstract

Dimensionality reduction based on appearance has been interesting issue on the face image research fields. Eigenface and Fisherface are linear techniques based on full spectral features, for both Eigenface and Fisherface produce global manifold structure. Inability of them in yielding local manifold structure have been solved by Laplacianfaces and further improved by Orthogonal Laplacianfaces, so it can yield orthogonal feature vectors. However, they have also a weakness, when training set samples have non-linear distribution. To overcome this weakness, feature extraction through data mapping from input to feature space using Gaussian kernel function is proposed. To avoid singularity, the Eigenface decomposition is conducted, followed by feature extraction using Orthogonal Laplacianfaces on the feature space, this proposed method is called Kernel Gaussian Orthogonal Laplacianfaces method. Experimental results on the Olivetty Research Laboratory (ORL) and the YALE face image databases show that, the more image feature and training set used, the higher recognition rate achieved. The comparison results show that Kernel Gaussian Orthogonal Laplacianfaces outperformed the other method such as the Eigenface, the Laplacianfaces and the Orthogonal Laplacianfaces.

Bahasa Abstract

Pemodelan Gaussian Orthogonal Laplacianfaces dalam Ruang Fitur untuk Pengenalan Citra Wajah. Reduksi dimensi berbasis penampakan telah menjadi isu menarik pada bidang penelitian citra wajah. Eigenface dan Fisherface merupakan teknik linier pada fitur-fitur spectral penuh, baik Eigenface Fisherface menghasilkan struktur manifold global. Ketidakmampuan struktur global dalam menghasilkan struktur manifold lokal telah dapat diselesaikan dengan nenggunakan Laplaciaface dan hasil perbaikannya yaitu Orthogonal Laplacianface, sehingga mampu menghasilkan vektor-vektor fitur orthogonal. Namun, metode tersebut juga mempunyai kelemahan ketika sampel data pelatihan mempunyai distribusi non linier. Untuk mengatasi kelemahan tersebut, diusulkan pemetaan data dari ruang input ke ruang fitur. Untuk menghindari singularity diusulkan dekomposisi Eigenface, diikuti dengan ekstraksi fitur menggunakan Orthogonal Laplacianface pada ruang fitur. Metode usulan ini disebut dengan Kernel Gaussian Orthogonal Laplacianface. Hasil-hasil eksperimen pada citra wajah basis data Olivetty Research Laboratory (ORL) dan YALE menunjukkan bahwa, semakin banyak fitur dan data pelatihan yang digunakan, semakin tinggi tingkat pengenalan yang diperoleh. Hasil perbandingan menunjukkan bahwa metode Kernel Gaussian Orthogonal Laplacianfaces mengungguli metode lain seperti Eigenface, Laplacianface, dan Orthogonal Laplacianface.

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