Generalized 2D principal component analysis for face image representation and recognition

Kong, Hui; Wang, Lei; Teoh, Eam Khwang; Li, Xuchun; Wang, Jiangang; Venkateswarlu, Ronda

doi:10.1016/j.neunet.2005.06.041

Cited by 167 publications

(123 citation statements)

References 15 publications

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“…Metode ini menilai secara langsung matrik within-class scatter dari matrik citra tanpa transformasi citra ke vektor, dan hal itu mengatasi singular problem dalam matrik within-class scatter (Gao, 2008). TDLDA menggunakan fisher criterion untuk menemukan proyeksi diskriminatif yang optimal (Kong, 2005). Metode TDLDA dengan klasifikasi Support Vector Machine (SVM) memiliki hasil yang optimal dibandingkan TDLDA KNN, 2DPCA, dan Fisherface (Damayanti, 2010).…”

Section: Pendahuluanunclassified

Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Musthafa

Fitrawan

Supria

2016

JBI

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Abstract. Face recognition is the identification process to recognize a person's face. Many studies have been developing face recognition methods, one of which is the Two Dimensional Linear Discriminant Analysis (TDLDA) which has pretty good accuracy results with the method of classification Support Vector Machine (SVM). With more training data can add computational time. TDLDA using all the piksel image as input to be processed for feature extraction. Though not all the objects in the area of the face is a significant feature in face recognition. In this study, the proposed use of the T-shape with only use a significant part is the eyes, nose, and mouth are integrated with TDLDA and SVM. The result could reduce computing time on face recognition 21.56% faster than TDLDA method. The accuracy of the results in this study was 91% -96% which is close to the level of accuracy without using a mask on the face.Keyword: face recognition, T-shape, TDLDA, Support vector machine.Â Abstrak. Pengenalan wajah merupakan proses identifikasi untuk mengenali wajah seseorang. Telah Banyak penelitian yang mengembangkan metode pengenalan wajah, salah satunya adalah Two Dimensional Linear Discriminant Analysis (TDLDA) yang memiliki hasil akurasi yang cukup baik dengan metode klasifikasi Support Vector Machine (SVM). Dengan semakin banyak data training dapat menambah waktu komputasinya. TDLDA menggunakan semua piksel citra sebagai masukan yang akan diproses untuk ekstrasi fitur. Padahal tidak semua objek pada area wajah merupakan fitur yang signifikan dalam pengenalan wajah. Dalam penelitian ini diusulkan penggunaan T-shape dengan hanya menyimpan bagian yang signifikan yaitu mata, hidung, dan mulut yang diintegrasikan dengan TDLDA dan SVM. Hasilnya dapat mengurangi waktu komputasi pada pengenalan wajah 21,56% lebih cepat daripada metode TDLDA. Hasil akurasi pada penelitian ini adalah 91%-96% yang mendekati tingkat akurasi tanpa menggunakan mask pada wajah.Kata Kunci: pengenalan wajah, T-shape, TDLDA, Support vector machine.

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Section: Pendahuluanunclassified

Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Musthafa

Fitrawan

Supria

2016

JBI

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“…, X n which are each of size r × c. It is frequently of interest to summarise the variability in the data by a linear projection to a lower-dimensional sub-space. Some recently developed and popular face recognition dimension reduction tools are 2DPCA (Yang et al 2004), matrix space image representations (Rangarajan 2001), bilateral-projection-based 2DPCA (B2DPCA) (Kong et al 2005), the generalized low rank approximation of matrices (GLRAM) (Ye 2005) and modular PCA (Gottumukkal and Asari 2004;Gao 2007).…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

“…An alternative dimension reduction technique for matrices is bilateral 2D principal components analysis (B2DPCA) (Kong et al 2005) which is equivalent to generalised low rank approximations of matrices (GLRAM) (Ye 2005), which in turn is equivalent to the earlier method of learning matrix space image representations (Rangarajan 2001). The task is again to find matrices of orthonormal columns A q , B s as above, but the aim is quite different in that low dimensional projections of the images themselves are required with little loss in information.…”

Section: Bilateral 2d Principal Components Analysismentioning

confidence: 99%

“…In face recognition and in many other applications it is of interest to summarise high-dimensional data using lower dimensional projections of the data, such as principal components analysis (PCA). In recent years there has been quite a large amount of interest in two-dimensional PCA (2DPCA) and various related methods (Yang et al 2004;Kong et al 2005;Ye 2005). The essential idea of the method is to carry out PCA acting directly on matrices rather than stacking vectors.…”

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confidence: 99%

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Factored principal components analysis, with applications to face recognition

Dryden

Brignell

et al. 2008

Stat Comput

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A dimension reduction technique is proposed for matrix data, with applications to face recognition from images. In particular, we propose a factored covariance model for the data under study, estimate the parameters using maximum likelihood, and then carry out eigendecompositions of the estimated covariance matrix. We call the resulting method factored principal components analysis. We also develop a method for classification using a likelihood ratio criterion, which has previously been used for evaluating the strength of forensic evidence. The methodology is illustrated with applications in face recognition.

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“…Kernel techniques can effectively to the traditional subspace methods for nonlinear improvement. Hui Kong et proposed a two-dimensional kernel principal component analysis (K2DPCA) method [5], which uses the kernel learning method not only to extract nonlinear features of human face but also to map the nonlinear inseparable face image into the high-dimensional feature space H , establishing the optimal hyper plane in H to realize the linear separable [6]. But PCA, 2DPCA and K2DPCA methods all do not make full use of class information of training samples [7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Kernel Two-dimensional Principal Component Analysis for Face Recognition

Zeng¹,

Chen²,

Tian³

et al. 2015

Advances in Intelligent Systems Research

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-The traditional kernel two-dimensional principal component analysis (K2DPCA) method did not take full advantage of the class information for face images and there are both "outer class" problem and "hard classifier" problem on face recognition. Therefore, a new face recognition method based on fuzzy kernel two-dimensional principal component analysis (FK2DPCA) is presented . Firstly, it introduces fuzzy concept into K2DPCA. Secondly, the class separability of criterion will be extended to high dimensional feature space by the use of kernel method. Furthermore, we select the eigenvectors that betweenclass scatter is greater than within-class scatter after projection as optimal projection axis. Finally, it uses the nearest neighbor classifier for face recognition . The experiment results on ORL and YALE face databases show that the FK2DPCA is better than other traditional methods.

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Generalized 2D principal component analysis for face image representation and recognition

Cited by 167 publications

References 15 publications

Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Factored principal components analysis, with applications to face recognition

Fuzzy Kernel Two-dimensional Principal Component Analysis for Face Recognition

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