Face Recognition in Ideal and Noisy Conditions Using Support Vector Machines, PCA and LDA

Oravec, Milos; Mazanec, Jan; Pavlovičová, Jarmila; Eiben, Pavel; Lehocki, Fedor

doi:10.5772/8943

Cited by 13 publications

(9 citation statements)

References 30 publications

(28 reference statements)

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“…Performing face recognition in the presence of noise and motion blur is a challenging task [40]. Hence we have developed a new robust score level fusion algorithm as follows:…”

Section: Proposed Score Level Fusion Algorithmmentioning

confidence: 99%

Robust Face Recognition in the Presence of Noises and Blurring Effects by Fusing Appearance Based Techniques and Sparse Representation

Fernandes

Bala

Nagabhushan

et al. 2013

2013 2nd International Conference on Advanced Computing, Networking and Security

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In real life, images obtained from video cameras or scanners are usually exposed to different levels of noises and blurring effects. In this paper we propose a new robust score level fusion technique to recognize faces in the presence of noise and blurring effects. The Proposed Score Level Fusion Technique (PSLFT) is obtained by using combinatory approach and ZScore normalization using the scores obtained from appearance based techniques: Principal Component Analysis (PCA), Fisherfaces (FF), Independent Component Analysis (ICA), Fourier Spectra (FS), Singular Value Decomposition (SVD) andSparse Representation (SR). The system is trained in the absence of noise, blurring effect but tested by imposing different levels of noises and blurring effects thus we have tried to imitate the real world scenarios. To investigate the performance of PSLFT, we simulate the real world scenario by adding noises: Median noise, Salt and pepper noise and also adding blurring effects: Motion blur and Gaussian blur. To evaluate performance of the PSLFT, we have considered six standard public face databases: IITK, ATT, JAFEE, CALTECH, GRIMANCE, and SHEFFIELD.

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“…Performing face recognition in the presence of noise and motion blur is a challenging task [40]. Hence we have developed a new robust score level fusion algorithm as follows:…”

Section: Proposed Score Level Fusion Algorithmmentioning

confidence: 99%

Robust Face Recognition in the Presence of Noises and Blurring Effects by Fusing Appearance Based Techniques and Sparse Representation

Fernandes

Bala

Nagabhushan

et al. 2013

2013 2nd International Conference on Advanced Computing, Networking and Security

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“…The first method is to select the optimal combination of the eigen-vectors rather than the largest ones [6] [7]. The second method is to transform the linear subspace into nonlinear subspace or higher dimensional space, i.e., kernel PCA and SVM [1][2] [8]. The last method is to select a proper distance measure for a specific application [7] [9].…”

Section: Introductionmentioning

confidence: 99%

A sequential subspace face recognition framework using genetic-based clustering

Deng

Mabu

Feng

et al. 2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Different from other classification problems, there are usually a large number of classes in the face recognition. As a result, the recognition accuracy of the traditional subspace face recognition algorithm is unsatisfactory. This paper presents a sequential subspace face recognition framework using an effective genetic-based clustering algorithm (GCA). Firstly, the facial database is decomposed into a double layer database using a face recognition oriented GCA. Then, the face recognition is realized by minimizing the distance measures in a specific cluster as in the traditional subspace face recognition algorithms. The contributions of this study are summarized as follows: 1) The class, i.e., person is regarded as an element in the clustering rather than an image. 2) The proposed GCA uses a novel distance to measure the similarity between a class and the cluster centroids of different clusters. 3) The proposed GCA uses a balance factor to achieve balanced clustering results. Experimental results on the extended Yale-B database indicate that the proposed sequential subspace face recognition framework has higher accuracy compared with the traditional subspace methods and K-mean+traditional subspace methods.

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“…Noise in face images can seriously affect the performance of face recognition systems (Oravec et al, 2010). Each image capturing generates digital or analog noise of diverse intensity.…”

Section: Modifications Of Face Images By Adding Gaussian Noisementioning

confidence: 99%

“…Features are first efficiently extracted by PCA with optimal truncating the vectors from the transform matrix. The parameters for the selection of the transformation vectors are based on our previous research (Oravec et al, 2010). The classification stage is performed by SVM.…”

Section: Pca+svmmentioning

confidence: 99%

“…The area of face recognition is well-described today in many papers and books, e.g. (Delac et al, 2008), (Li & Jain, 2005), (Oravec et al, 2010). The idea that two-dimensional still-image face recognition in controlled environment is already a solved task is generally accepted and several benchmarks evaluating recognition results were done in this area (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition

Oravec¹,

Pavlovičová²,

Mazanec³

et al. 2011

Reviews, Refinements and New Ideas in Face Recognition

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Face Recognition in Ideal and Noisy Conditions Using Support Vector Machines, PCA and LDA

Cited by 13 publications

References 30 publications

Robust Face Recognition in the Presence of Noises and Blurring Effects by Fusing Appearance Based Techniques and Sparse Representation

Robust Face Recognition in the Presence of Noises and Blurring Effects by Fusing Appearance Based Techniques and Sparse Representation

A sequential subspace face recognition framework using genetic-based clustering

Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition

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