Face Detection Based on Two Dimensional Principal Component Analysis and Support Vector Machine

Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

Although two-dimensional principal component analysis (2DPCA) extracts image features directly from 2D image matrices rather than one dimensional vectors, 2DPCA is only based on the whole images to preserve total variances by maximizing the trace of feature covariance matrix. Thus, 2DPCA cannot extract localized components, which are usually important for face recognition. Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition called two-dimensional nonnegative principal component analysis (2DNPCA) to extract localized components and maintain the maximal variance property of 2DPCA. 2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, 2DNPCA has both advantages of 2DPCA and NMF. Furthermore, 2DNPCA solves the time-consuming problem by removing the restriction of minimizing the cost function and extracting only the base matrix. The nearest neighbor (NN) classifier and linear regression (LR) classifier are used for classification and extensive experimental results show that 2DNPCA plus NN and 2DNPCA plus LR are both very efficient approaches for face recognition.

This paper presents a comparison between one-dimensional component analysis (1D-PCA) and two-dimensional principal component analysis (2D-PCA) under two different types of classification techniques namely k-nearest neighbor (kNN) and Support Vector Machines (SVM). These two techniques differ in the method to determine the image covariance matrix. 2DPCA used 2D image matrices instead of column vectors in 1DPCA. The eigenvectors derived from these matrices will result in images in reduced dimensions to be used for classification. K-nearest neighbor algorithm (kNN) and Support Vector Machines (SVM) were used for classification. The performance measures used for comparison were classification accuracy and computational time. The tests were performed on the ORL image database. The experimental results indicate that 2DPCA outperforms in terms of classification accuracy and computational complexity.

One of the widely used approaches in image recognition is principal component analysis (PCA) because of the good balance between the simplicity and speed of the algorithm and the results obtained by using it. In the last years many variants of PCA were developed: two dimensional PCA, two directional two dimensional PCA, extended two dimensional PCA and extended two dimensional two directional PCA, the last one developed by the first two authors of the present paper. In this paper we go further with this study by considering a mixed approach between E2DPCA and diagonal PCA. The mixed approach not only takes approximately the same amount of time for training and testing as the classical approach, but also gives better recognition accuracy for some of the PCA algorithm variants.

Face recognition is one of the most important part of biometrical recognition. 2-Dimensional Principal Component Analysis (2DPCA) is a classic method in face recognition, which is proposed to reduce the computational cost of the standard Principal Component Analysis (PCA) algorithm, but the performance of 2-Dimensional Principal Component Analysis in reducing computational complexity and recognition rate is not satisfying. This paper mainly focuses on the feature extraction method of adaptively weighted Block 2-Dimensional Principal Component Analysis. The block methods divide a large picture into several smaller sub-blocks to get the local discrimination information and reduce the computational complexity. Then, a weighted Euclidean distance classifying algorithm is proposed to extract features of face images, and the Euclidean distance classifier is used for classifying. The experiments show that the Adaptively Weighted Block 2-Dimensional Principal Component Analysis method has better performance than standard 2-Dimensional Principal Component Analysis.

Customers should have several benchmarks to buy banana from the market. One of them is observing each size to its ripeness. This study present a framework for determining bananas based on types and levels of ripeness from banana peel and images. We use three machine learning method, i.e. $\boldsymbol{k}$ -Nearest Neighbor ( $\boldsymbol{k}-\boldsymbol{NN}$ ), Support Vector Machine (SVM), and Decision Tree (DT). The banana is placed on the white background and photographed within 0.6 meters with 17 different position. The images are converted into grayscale mode and become 96x96 pixels. Principal Component Analysis (PCA) is conducted to reduce the dimensionality from 9,216 pixels to 236 pixels and 128 pixels. In this research, SVM is able to provide high accuracy compared to other methods, $\boldsymbol{k}-\boldsymbol{NN}$ and DT, to determine banana types, that is 99.1%. To determine the level of ripeness, $\boldsymbol{k}-\boldsymbol{NN}$ and SVM achieved the same highest result, that is 96.6%. However, SVM has the faster processing time compared to $\boldsymbol{k}-\boldsymbol{NN}$ , that is 5.517s. Furthermore, SVM is also tested by using PCA 256 pixels, PCA 128 pixels, and non-PCA. The result was SVM with PCA 128 pixels was able to reduce the processing time from 5.517s to 5.492s.

Dimensionality reduction methods play an important role in face recognition. Principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference (CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when 2DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.

Aiming at Shenzhen component index in the stock market change trend prediction problem, a new method based on Principal component analysis and Support vector machine was proposed. Principal component analysis was used to calculate the influence factors of the share price of Shenzhen component index and the closing price of Shenzhen component index, and then the influence factors were reduced from high dimension to low dimension. From 1991 to 2018, More than 6,700 groups of data related to the Shenzhen component index were selected to test the method. The average relative error of the Shenzhen component index closing price calculated is 7.35%, the experimental results showed that. The convergence speed and accuracy of the experiment are improved by the principal component analysis method. As a result, based on principal component analysis and support vector machine, it can be able to make use of the data of known Shenzhen component index to speculate on the trend of closing prices. And it has a certain application prospect in the field of stock forecasting research.

In this paper, based on the study of the Two-Dimensional Principal Component Analysis (2DPCA), Two-Dimensional Principal Component Analysis (2DPCA) and fuzzy set theory, we propose a integrated face recognition algorithm based on wavelet subspace. This method can make good use of the advantages of each single method, and also can make up for the defect of each other. The comparison of the results of the different methods identification effect on the ORL、YALE and FERET face database show, the integrated method proposed in this paper improves the recognition rate, and it also reduces the training and classification time as well. Key word: face recognition; Two-Dimensional Principal Component Analysis (2DPCA); Two-Dimensional Linear Discriminant Analysis (2DLDA); fuzzy set theory; feature extraction be divided into several categories:the method based on geometric features、the method based on model、the method based on the statistical、the method based on neural network method and the method of combining multiple classifiers, this paper mainly focuses on the method based on statistics. The method based on statistical faced the image as random vector, thus using some statistical methods to analyze the face model, the most representative methods include the eigenface method based on principal component analysis which proposed by Turk M (2)

In this paper, a method of shellfish recognition based on Gabor transform and two-dimensional image principal component analysis (2DPCA) is studied. Gabor transform is used to extract the image features and determine the dimension of the image features; The 2DPCA method is used to reduce the dimension of the transformed features. Compared with BP neural network and support vector machine (SVM) experiments, the results shows that the extreme learning machine classifier is very fast, which has good generalization and high accuracy.

In this paper we propose an efficient algorithm based on principal component analysis (PCA) and fuzzy support vector machine (SVM) for the diagnosis of breast cancer tumor. First, PCA algorithm is implemented to project high-dimensional breast tumor data into much lower dimensional space, then the processed data are classified by a fuzzy SVM classifier. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method can greatly speed up the training and testing of the classifier, get high testing correct rate and pick out untypical cases to be reexamined by experienced doctors, superior to the traditional rigid margin SVM classifier.

The equivalence of two-dimensional PCA to line-based PCA

This paper presents the basis-based speaker adaptation method that includes approaches using principal component analysis (PCA) and two-dimensional PCA (2DPCA). The proposed method partitions the hidden Markov model (HMM) mean vectors of training models into subvectors of smaller dimension. Consequently, the sample covariance matrix computed using the partitioned HMM mean vectors has various dimensions according to the dimension of the subvectors. From the eigen-decomposition of the sample covariance matrix, basis vectors are constructed. Thus, the dimension of basis vectors varies according to the dimension of the sample covariance matrix, and the proposed method includes PCA and 2DPCA-based approaches. We present the adaptation equation in both the maximum likelihood (ML) and maximum a posteriori (MAP) frameworks. We perform continuous speech recognition experiments using the Wall Street Journal (WSJ) corpus. The results show that the model with basis vectors whose dimensions are between those of PCA and 2DPCA-based approaches shows good overall performance. The proposed approach in the MAP framework shows additional performance improvement over the ML counterpart when the number of adaptation parameters is large but the amount of available adaptation data is small. Furthermore, the performance of the approach in the MAP framework approach is less sensitive to the choice of model order than the ML counterpart.

Detection of asphyxia in infant at an early stage is crucial to reduce the rate of infant morbidity. The information regarding asphyxia can be extracted from infant cry signals using support vector machine (SVM) combined with effective feature selection methods such as principal component analysis (PCA) or orthogonal least square (OLS). The performance of SVM in recognizing infant cry with asphyxia after undergone comprehensive identification of optimal parameters at the feature extraction and classification stages has not been reported. This paper describes the two stages of optimal parameter identification; at Mel-frequency Cepstral coefficient (MFCC) analysis stage, SVM with and without employing the PCA and OLS stages, and the performance of the SVM in recognizing infant cry with asphyxia resulted from all levels of optimal parameters identification. The SVM was first optimized after performing MFCC analysis to find the optimum parameters. Two types of kernels were used, the polynomial and RBF kernels. The subsequent SVM optimizations were conducted after PCA and OLS were employed. In the PCA, the significant features were selected using eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE). The SVM performance was evaluated based on classification accuracy and computation time. The experimental results have shown that the optimized SVM was able to recognize the asphyxiated infant cry with an accuracy of 94.84% and computation time of 1.98s using PCA with EOC and RBF kernel.

This paper aims to improve recognition accuracies of single sources of partial discharge (PD) by employing image-oriented feature extraction and selection algorithms. 419 diversified samples of PD data acquired from typically artificial defect models, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithms validation. In the proposed method, the PD gray images are decomposed into various vectors by two-dimensional principal component analysis (2DPCA) on horizontal and vertical directions, respectively, in which 9 representative parameters are extracted from each image vector. Based on the 419 PD samples, the proposed image features are confirmed to achieve better PD recognition performances than typical image compression methods relevant to 2DPCA. To further improve the recognition performance and reduce the feature dimension, the feature selection technique based on non-dominated sorting genetic algorithm II (NSGA-II) is adopted to search the optimal 2DPCA features. By using the proposed image-oriented feature extraction and selection algorithm, fuzzy k-nearest neighbor classifier (FkNNC), back-propagation neural network (BPNN) and support vector machine (SVM) with 16-dimensional 2DPCA features can now achieve 96.4%, 94.4% and 97.5% accuracies, respectively. The increment of average recognition accuracies of 5%-7% are obtained by different classifiers compared with phase-resolved partial discharge (PRPD) statistical features in previous studies. The investigations on the recognition results relevant to image distortion, cavity discharge change with time and PD contaminated by random noises with different level confirm the robustness of the 16-dimensional 2DPCA features, which signify a great potential in applying the proposed method to field PD pattern recognition in the future.