Arif Muntasa
Universitas Trunojoyo

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The Gaussian Orthogonal Laplacianfaces Modelling in Feature Space for Facial Image Recognition Muntasa, Arif
Makara Journal of Technology Vol 18, No 2 (2014)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.047 KB) | DOI: 10.7454/mst.v18i2.399


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.
Jurnal Informatika Vol 13, No 1 (2015): MAY 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (667.819 KB) | DOI: 10.9744/informatika.13.1.9-14


Image security is a process to save digital. One method of securing image digital is watermarking using Least Significant Bit algorithm. Main concept of image security using LSB algorithm is to replace bit value of image at specific location so that created pattern. The pattern result of replacing the bit value of image is called by watermark. Giving watermark at image digital using LSB algorithm has simple concept so that the information which is embedded will lost easily when attacked such as noise attack or compression. So need modification like development of LSB algorithm. This is done to decrease distortion of watermark information against those attacks. In this research is divided by 6 process which are color extraction of cover image, busy area search, watermark embed, count the accuracy of watermark embed, watermark extraction, and count the accuracy of watermark extraction. Color extraction of cover image is process to get blue color component from cover image. Watermark information will embed at busy area by search the area which has the greatest number of unsure from cover image. Then watermark image is embedded into cover image so that produce watermarked image using some development of LSB algorithm and search the accuracy by count the Peak Signal to Noise Ratio value. Before the watermarked image is extracted, need to test by giving noise and doing compression into jpg format. The accuracy of extraction result is searched by count the Bit Error Rate value.
Jurnal Informatika Vol 13, No 1 (2015): MAY 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (389.347 KB) | DOI: 10.9744/informatika.13.1.1-8


Digital image has size and object in the form of foreground and background. To separate it, it is necessary to be conducted the image segmentation process. Otsu thresholding method is one of image segmentation method. In this research is divided into five processes, which are input image, pre-processing, segmentation, cleaning, and accuracy calculation. First process was input color images which consists of multiple objects. Second process was conversion from color image to grayscale image. Third process was automatically calculated threshold value using Otsu thresholding method, followed by binary image transformation. The fourth process, the result of third process is changed into negative image as the segmentation results, noise removal with a threshold value of 150, and morphology. The last accuracy calculation is conducted to measure proposed segmentation method performance. The experimental result have been compared to the image of Ground Truth as the direct user observation to calculate accuracy. To examine the proposed method, Weizmann Segmentation Database is used as data set. It conconsist of 30 color images. The experimental results show that 93.33% accuracy were achieved.
Appearance Global and Local Structure Fusion for Face Image Recognition Arif Muntasa; Indah Agustien Sirajudin; Mauridhi Hery Purnomo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 1: April 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v9i1.678


Principal component analysis (PCA) and linear descriminant analysis (LDA) are an extraction method based on appearance with the global structure features. The global structure features have a weakness; that is the local structure features can not be characterized. Whereas locality preserving projection (LPP) and orthogonal laplacianfaces (OLF) methods are an appearance extraction with the local structure features, but the global structure features are ignored. For both the global and the local structure features are very important. Feature extraction by using the global or the local structures is not enough. In this research, it is proposed to fuse the global and the local structure features based on appearance. The extraction results of PCA and LDA methods are fused to the extraction results of LPP. Modelling results were tested on the Olivetty Research Laboratory database face images. The experimental results show that our proposed method has achieved higher recognation rate than PCA, LDA, LPP and OLF Methods.