When Uncorrelated Linear Discriminant Analysis Are Combined with Wavelets

Spatial domain facial recognition Modular IMage Principal Component Analysis (MIMPCA) has an improved recognition rate compared to the conventional PCA. In the MPCA, face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. In this work, the Transform Domain implementation of MPCA is presented. The facial image has two representations. The Two Dimensional MPCA (TD - 2D - MPCA) and the Diagonal matrix MPCA (TD - Dia - MPCA). The sub-images are processed using both non-overlapping and overlapping windows. All the test results, for noise free and noisy images, using ORL, Yale and FERET databases achieved; 99.5%, 99.58% and 97.42% recognition accuracy respectively. Transform Domain implementations yield, computational and storage savings of at least 75% and 99.98%, respectively, compared to spatial domain. Sample results are given.

This paper focuses on the problem of environmental noises in human-human communication and in automatic speech recognition. To deal with this problem, the use of alternative acoustic sensors -which are attached to the talker and receive the uttered speech through skin or bones- is investigated. In the current study, throat microphones and ear bone microphones are integrated with standard microphones using several fusion methods. The results obtained show that the recognition rates in noisy environments are drastically increased when these sensors are integrated with standard microphones. Moreover, the system does not show any recognition degradations in clean environments. In fact, recognition rates also increase slightly in clean environments. Using late fusion to integrate a throat microphone, an ear bone microphone, and a standard microphone, we achieved a 44% relative improvement in recognition rate in a noisy environment and a 24% relative improvement in recognition rate in a clean environment.

In this research, we selected the speech recognition to implement the electric wheelchair system as a method to control it by only using the speech and used DTW (Dynamic Time Warping), which is speaker-dependent and has a relatively high recognition rate among the speech recognitions. However, it has to have small memory and fast process speed performance under consideration of real-time. Thus, we introduced VQ (Vector Quantization) which is widely used as a compression algorithm of speaker-independent recognition, to secure fast recognition and small memory. However, we found that the recognition rate decreased after using VQ. To improve the recognition rate, we applied ART2 (Adaptive Reason Theory 2) algorithm as a post-process algorithm to obtain about 5% recognition rate improvement. To utilize ART2, we have to apply an error range. In case that the subtraction of the first distance from the second distance for each distance obtained to apply DTW is 20 or more, the error range is applied. Likewise, ART2 was applied and we could obtain fast process and high recognition rate. Moreover, since this system is a moving object, the system should be implemented as an embedded one. Thus, we selected TMS320C32 chip, which can process significantly many calculations relatively fast, to implement the embedded system. Considering that the memory is speech, we used 128kbyte-RAM and 64kbyte ROM to save large amount of data. In case of speech input, we used 16-bit stereo audio codec, securing relatively accurate data through high resolution capacity.

In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same ( 90 % ) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

Current automatic facial recognition systems are not robust against changes in illumination, pose, facial expression and occlusion. In this paper, we propose an algorithm based on a probabilistic approach for face recognition to address the problem of pose change by a probabilistic approach that takes into account the pose difference between probe and gallery images. By using a large facial image database called CMU PIE database, which contains images of the same set of people taken from many different angles, we have developed a probabilistic model of how facial features change as the pose changes. This model enables us to make our face recognition system more robust to the change of poses in the probe image. The experimental results show that this approach achieves a better recognition rate than conventional face recognition methods over a much larger range of pose. For example, when the gallery contains only images of a frontal face and the probe image varies its pose orientation, the recognition rate remains within a less than 10% difference until the probe pose begins to differ more than 45 degrees, whereas the recognition rate of a PCA-based method begins to drop at a difference as small as 10 degrees, and a representative commercial system at 30 degrees.

An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem of pose variation and illumination change for face recognition is proposed. The system has combined 2D and 3D systems in the feature level which presents higher performance in contrast with methods which utilizes either 2D or 3D system separately. A multimodal face recognition based on Modular PCA when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in illumination and facial expression. The proposed algorithm is tested with FRAV3D database that has faces with pose variation and illumination changes. Recognition rates from experimental results show the superiority of Modular PCA over conventional PCA methods in tackling face images with different pose variations and changes in illuminations. The proposed algorithm shows a recognition rate of 86% that is achieved in fusion experiment.

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

Local appearance-based methods have been proposed recently for face recognition. We analyze the effects of different dimension reduction and normalization methods on local appearance-based face recognition in this paper. Each image is divided into equal sized blocks and six different dimension reduction methods are implemented for each block separately to create local visual feature vectors. On these local features, several normalization methods are applied in an attempt to eliminate the changes in lighting conditions and contrast differences among blocks of different face images. The experimental results show the improvements in recognition rates due to the effects of dimension reduction and normalization for three different classifiers. Usage of trainable dimension reduction methods instead of DCT and a new normalization method in our work (within-block normalization as referred in this paper) are two factors that makes difference from previous works in literature. The best performance is achieved using a block size of 16times16, performing dimension reduction using approximate pairwise accuracy criterion (aPAC) and applying within-block mean and variance normalization.

There are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to improve the performance of recognizing faces in the case of existing high-density noise in faces images. In this paper, Median filter, Hybrid Median Filter, Adaptive Median filter, and Adaptive Weighted Mean Filter were used to remove the noise from the faces images, and they were compared in order to use the best of these filters as a pre-processing step before the face recognition process. Experimental results showed that the Adaptive Weighted Mean Filter gave better results compared with the other filters. Thus, the performance of face recognition process was improved under high-density noise using the Adaptive Weighted Mean Filter and Principal Component Analysis. For the corrupted images by 90 % noise density, Recognition rate by using Median Filter reached 0% and 33% by using Hybrid Median Filter. While Recognition rate by using the Adaptive Median Filter and Adaptive Weighted Mean Filter reached 100%.

This article discusses vision recognition process and finds out that human recognizes objects not by their isolated features, but by their main difference features which people get by contrasting them. According to the resolving character of difference features for vision recognition, the difference feature neural network(DFNN) which is the improved auto-associative neural network is proposed. Using ORL database, the comparative experiment for face recognition with face images and the ones added Gaussian noise is performed, and the result shows that DFNN is better than the auto-associative neural network and it proves DFNN is more efficient.

This paper describes phone modelling improvements to the hybrid connectionist-hidden Markov model speech recognition system developed at Cambridge University. These improvements are applied to phone recognition from the TIMIT task and word recognition from the Wall Street Journal (WSJ) task. A recurrent net is used to map acoustic vectors to posterior probabilities of phone classes. The maximum likelihood phone or word string is then extracted using Markov models. The paper describes three improvements: connectionist model merging; explicit presentation of acoustic context; and improved duration modelling. The first is shown to provide a significant improvement in the TIMIT phone recognition rate and all three provide an improvement in the WSJ word recognition rate. >

Recognition of car horns based on principal component analysis of MEL frequency Cepstral coefficients and support vector machine

This paper proposes a novel preprocessing technique in order to improve the performance of a Face Recognition (FR) system. The proposed Edge-based Scale Normalization (ESN) process involves the use of scale normalization along with edge detection as a preprocessing technique in order to eliminate unwanted background details in face images. Feature extraction is performed on the preprocessed image using Discrete Fourier Transform (DFT). The DFT spectrums of these images extract the low frequency coefficients required for face recognition. These important features are selected through a rhombus-shaped mask around the center of the DFT spectrum. Further optimization in feature selection is achieved through Binary Particle Swarm Optimization (BPSO) technique. Experimental results, obtained by applying the proposed algorithm on Cambridge ORL and Extended YaleB face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features is observed. Significant dimensionality reduction by more than 98.5% and improved recognition rate of 98% are achieved for both datasets. ∗Corresponding author, E-mail: kmanikantan@msrit.edu †E-mail: ramachandr@gmail.com DFT domain Feature Extraction using Edge-based Scale Normalization for Enhanced Face Recognition 135

A hidden Markov model (HMM) is a natural and highly robust statistical method for automatic speech recognition. It has been tested and proved effective in a wide range of applications. The HMM model parameters are used to describe the utterance of the speech segment presented by the HMM. Many successful heuristic algorithms are developed to optimize the model parameters to best describe the training observation sequences. However, all these methods are exploring for only one local maximum in practice. No single method can be recovered from the local maximum and to obtain the global maximum or other more optimized local maxima. In this paper, a stochastic search method called the genetic algorithm (GA) is presented for HMM training. GA mimics natural evolution and performs searching within the defined searching space. Experimental results showed that using GA for HMM training (GA-HMM training) can obtain better solutions than using heuristic algorithms. One of the major drawbacks is that GAs require a lot of computation power for global searching before it can converge. Therefore, in order to outperform heuristic algorithms, a parallel version of GA called the parallel genetic algorithm (PGA) is introduced. Experimental results showed that using PGA in speech recognition systems provides 18% improvement in recognition rate with the same amount of computational time.