Hybrid independent component analysis and support vector machine learning scheme for face detection

Structural damage detection is very important for identifying and diagnosing the nature of the damage in an early stage so as to reduce catastrophic failures and prolong the service life of structures. In this paper, a novel approach is presented that integrates independent component analysis (ICA) and support vector machine (SVM). The procedure involves extracting independent components from measured sensor data through ICA and then using these signals as input data for a SVM classifier. The experiment presented employs the benchmark data from the University of British Columbia to examine the effectiveness of the method. Results showed that the accuracy of damage detection using the proposed method is significantly better than the approach by integrating ICA and ANN. Furthermore, the prediction output can be used to identify different types and levels of structure damages.

Spatiotemporal analysis of the groundwater head variation caused by natural stimuli using independent component analysis and continuous wavelet transform

The Support Vector Machine (SVM) (Cortes and Vapnik, 1995; Vapnik, 1995; Burges, 1998) is intended to generate an optimal separating hyperplane by minimizing the generalization error without the assumption of class probabilities such as Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called Support Vectors (SVs). In practice, these SVMs are a subset of the entire training data. By now, SVMs have been successfully applied in many applications, such as face detection, handwritten digit recognition, text classification, and data mining. Osuna et al. (1997) applied SVMs for face detection. Heisele et al. (2004) achieved high face detection rate by using 2nd degree SVM. They applied hierarchical classification and feature reduction methods to speed up face detection using SVMs. Feature extraction and reduction are two primary issues in feature selection that is essential in pattern classification. Whether it is for storage, searching, or classification, the way the data are represented can significantly influence performances. Feature extraction is a process of extracting more effective representation of objects from raw data to achieve high classification rates. For image data, many kinds of features have been used, such as raw pixel values, Principle Component Analysis (PCA), Independent Component Analysis (ICA), wavelet features, Gabor features, and gradient values. Feature reduction is a process of selecting a subset of features with preservation or improvement of classification rates. In general, it intends to speed up the classification process by keeping the most important class-relevant features.

In recent years, the searching and indexing techniques for multimedia data are getting more attention in the area of multimedia databases. As many research works were done on the content-based retrieval of image and video data, less attention was received to the content-based retrieval of audio data. Audio is one of important multimedia information and there is a growing need for automatic audio indexing and retrieval techniques in recent years. Audio data contain abundant semantics and the audio signal processing can reduce computational complexity, so effective and efficient indexing and retrieval techniques for audio data are getting more attention. In this paper, problems of audio retrieval are discussed firstly. Then, main audio characteristics and features are introduced. Finally the combination of Support Vector Machine and General Model is described and the hybrid model is used in audio retrieval. Experiments show that the hybrid model is effective for audio classification.

A hybrid support vector machine (SVM) and hidden Markov model (HMM) approach is proposed for designing continuous speech recognition systems. Using novel properties of SVMs and combining them with HMMs one can obtain models that map easily to hardware and lead to more modular and scalable design. The overall architecture of the proposed system is based on the MAP (maximum a posteriori) framework which offers a direct, feedforward recognition model. The SVMs generate smooth estimates of local transition probabilities in the HMM, conditioned on the acoustic inputs. The transition probabilities are then used to estimate the global posterior probabilities of HMM state sequences. A parallel architecture that implements a simple speech recognition model in real-time is presented.

The paper explores the hybrid of SVM and system of Artificial Neural Network as the finest binary classification system for calculating the diabetic nature of people in comparison to Support Vector Machine (SVM). In this research, the sets of all the parameters describing the diabetic nature of a person are taken from the laboratories. This approach is chosen so that a better learning method can be used for various problems. The testing results were found to be in accordance with the accepted results that resemble with the direct diagnosis of a physician. The results of this research shows that this hybrid SVM and Artificial Neural Network (ANN) model is more precise than the SVM model. These results of the hybrid SVM and ANN model suggest that it is very effective for the classification of Diabetic and Non Diabetic nature of a person. This paper highlights the concept of Support Vector Machines and its integration with Artificial Neural Network, the two key characteristics with one being the generalization theory of the SVM model that best describes how to select a hypothesis and functions given by Kernel that introduces the idea of non-linearity without the inclusion of the actual algorithm.

Research on facial expression recognition is critical for personalized human-computer interaction (HCI). Recent advances in localized, sparse and discriminative image feature descriptors have been proven to be promising in visual recognition, both statically and dynamically, making it quite useful for facial expression recognition. In this paper we show that the independent Log-Gabor feature (IGF), a localized and sparse representation of pattern of interest, can perform conveniently and satisfactorily for facial expression recognition task. In low-level feature extraction, Log-Gabor wavelet features are extracted, then ICA is applied to produce independent image bases that reduce the redundancy, emphasize edge information, while preserving orientation and scale selection property in the image data. In high-level classification, SVM classifies the propagated independent Log-Gabor features features as discriminative components. We demonstrate our algorithm on facial expression databases for recognition tasks, showing that the proposed method is accurate and more efficient than current approaches.KeywordsICAGabor WaveletFacial Expression

Computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. We have developed a gender classifier with performance superior to existing gender classifiers. This paper addresses the problem of gender classification using frontal facial images. The testbed consists of 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database. Independent component analysis (ICA) is used to represent each image as a feature vector in a low dimensional subspace. Different classifiers are studied in this lower dimensional subspace. Our experimental results show the best accuracy of 96% in gender classification by combining ICA and support vector machines (SVMs).

Structure health monitoring detects the nature of structure damage in an early stage by the network sensors whose signals are normally highly correlated and mixed with noise. Feature reduction methods are applied in extracting attributes, that will be input into advanced classification models. The complicated data transformation and classification procedures make it difficult to identify direct relationship between sensors and abnormal dynamic structure characteristics, especially for complex buildings with large numbers of sensors. In this study, the sensor sensitivity analysis on a structure damage identifier is applied, which integrates independent component analysis (ICA) and artificial neural network (ANN) together. The approach is evaluated on a benchmark data from University of British Columbia. The experimental results show sensitivity analysis not only helps domain experts understand the mapping from different location and type of sensors to a damage class, but also significantly reduce noise and increase the accuracy of ICA-ANN classifier.

Hypertension is a chronic disease that often goes undetected in its early stages, increasing the risk of complications such as stroke and heart failure. Accurate classification of hypertensive patients is essential to support early intervention and reduce morbidity and mortality. This study aims to evaluate the performance of the Support Vector Machine (SVM) algorithm and to develop a hybrid classification model by integrating Particle Swarm Optimization (PSO) to improve the predictive performance of SVM. The research was conducted using 400 patient records from Medika Palopo General Hospital, equally divided into hypertensive and non-hypertensive groups, with 12 clinical features as input variables. The SVM model was built using a sigmoid kernel with default parameters (C = 1.0, gamma = auto), while the hybrid model utilized PSO to optimize the values of C and gamma. Evaluation results show that the conventional SVM model achieved an accuracy of 61.25%, precision of 63.22%, recall of 56.50%, F1-score of 59.31%, and AUC of 0.6400. After optimization using PSO, the hybrid model significantly improved with an accuracy of 96.75%, precision of 97.14%, recall of 96.31%, F1-score of 96.73%, and AUC of 0.9725. The findings indicate that the hybrid SVM-PSO approach effectively enhances the classification performance of hypertension prediction models and offers promising potential to be developed into an AI-based medical decision support system.

Describes a pattern classification approach for detecting frontal-view faces via learning a decision boundary. The classification can be achieved either by explicit estimation of density functions of two classes, face and non-face or by direct learning of a classification function (decision boundary). The latter is a more effective approach, when the number of training available examples is small, compared to the dimensionality of image space. The proposed method consists of a implicit modeling of both face and near-face classes using Independent Component Analysis (ICA), and a subsequent classification stage based on the decision boundary estimation using Support Vector Machine (SVM). Multiple nonlinear SVMs are trained for local subspaces, considering the general non-Gaussian and multi-modal characteristic of face space. This parallelization of SVMs reduces computational cost of on-line classification, since the locally trained SVM has small number of support vectors compared to the SVM trained on entire data space. We showed that the proposed algorithm is superior to the simple combination of ICA and SVM, both in accuracy and computational burden.

In order to improve the generalization performance of support vector machine (SVM), a support vector machine ensembling method based on independent component analysis (ICA) and fuzzy kernel clustering (FKC) was proposed. The ICA emphasizes the independence between the data characteristics and can effectively obtain a series of independent features, the performance of single SVM can be improved when the SVM was trained on these independent features; The FKC method uses kernel function to expand the feature space, so that the clustering is more accurate and the diversity between sub-SVMs can also be guaranteed. Simulation results on UCI datasets show that the proposed ensembling method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.

Machine learning techniques have shown considerable promise for automating common visual inspection tasks such as the detection of human faces in cluttered scenes. Here, we examine whether similar techniques can be used (or adapted) for the problem of automatically locating geologic landforms in planetary images gathered by spacecraft. Beyond enabling more efficient and comprehensive ground analysis of down-linked data, we are aiming toward perceptive spacecraft that use onboard processing to autonomously analyze their collected imagery and take appropriate actions. In our current study, we have employed various supervised learning algorithms, including neural networks, ensemble methods, support vector machines (SVM), and continuously-scalable template models (CSTM) to derive detectors for craters from ground-truthed images. The resulting detectors are evaluated on a challenging set of Viking Orbiter images of Mars containing roughly one thousand craters. The SVM approach with normalized image patches provides detection and localization performance closest to that of human labelers and is shown to be substantially superior to boundary-based approaches such as the Hough transform. However, the run-time cost in applying the SVM solution in the standard way (spatial scanning in which the SVM is applied to each patch of the image on a window-by-window basis) is too high due both to the number of support vectors required and the number of test vectors generated by sliding a window across the data. We have developed an implementation using FFTs and the overlap-and-add technique, which can be used to efficiently apply SVMs to sensor data in resource-constrained environments such as on a spacecraft. The technique allows exact computation of the SVM decision function over an image using minimal RAM (typically less than 5% of the size of the image) and only ${\mathcal{O}}(n_{s} (\log_{2} d + 11))$ real multiplications per pixel, where n s is the number of support vectors and d is the dimensionality of the vectors compared with ${\mathcal{O}}(n_{s} d)$ real multiplications per pixel for spatial scanning. Our approach is complementary to reduced set methods providing (in theory) a multiplicative gain in performance.

Software project development has high failure rate. Software project risk management may gain a high rate of return in investment. Establishing an intelligent risk evaluation model for project will be valuable in the analysis and control of project risks. In this paper, we employed neural network (NN) and support vector machine (SVM) approaches to establish a model for risk evaluation in project development. In the model, the input is a vector of software risk factors that were obtained through interview with 30 experts, and the output is the final outcome of the project. The data for modeling were collected from 120 real software projects through questionnaires. The experiment shows the model is valid. Interestingly, SVM is a powerful supervised learning method, and some believe that it is a more promising classification method that may someday supercede NN. In our study, the standard neural network model had lower prediction accuracy compared to SVM due to its tendency in finding local optima. However, after attempt in optimizing the neural network model with genetic algorithm, the experimental results showed that our enhanced model surpassed SVM in performance.

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.