Sharif Digital Repository

In this paper, we present a novel multilinear algebra based feature extraction approach for face recognition which preserves some implicit structural or locally-spatial information among elements of the original images. We call this method three-dimensional modular discriminant analysis (3DMDA). Our approach uses a new data model called third-order tensor model (3TM) for representing the face images. In this model, each image is partitioned into the several equal size local blocks, and the local blocks are combined to represent the image as a third-order tensor. Then, a new optimization algorithm called direct mode (d-mode) is introduced for learning three optimal projection axes. Extensive... 

One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with heteroscedastic data. In this paper, we present a novel two-dimensional feature extraction technique for face recognition which is capable of handling the heteroscedastic data in the dataset. The technique is a general form of two-dimensional linear discriminant analysis. It generalizes the interclass scatter matrix of two-dimensional LDA by applying the Chernoff distance as a measure of separation of every pair of clusters with the same index in different classes. By employing the new distance, our method can capture the discriminatory information presented in the difference of covariance... 

Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model leads to the two-dimensional canonical correlation directions. This model helps for better understanding of two-dimensional Canonical Correlation Analysis (2DCCA), and for further extending the method into more complex probabilistic model. In addition, we show that two-dimensional Linear Discriminant... 

There is a growing attention in subspace learning using tensor-based approaches in high dimensional spaces. In this paper we first indicate that these methods suffer from the Heteroscedastic problem and then propose a new approach called Heteroscedastic Multilinear Discriminant Analysis (HMDA). Our method can solve this problem by utilizing the pairwise chernoff distance between every pair of clusters with the same index in different classes. We also show that our method is a general form of Multilinear Discriminant Analysis (MDA) approach. Experimental results on CMU-PIE, AR and AT&T face databases demonstrate that the proposed method always perform better than MDA in term of classification... 

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected... 

This paper has presented a novel discriminative parameters calibration approach based on the Model Distance Maximizing (MDM) to improve the performance of our previous proposed robustness method named spectral subtraction (SS) in likelihoodmaximizing framework. In the previous work, for adjusting the spectral over-subtraction factor of SS, conventional ML approach is used that only utilizes the true model without considering other confused models. This makes it very probably to reach a suboptimal solution. While in MDM, by maximizing the dissimilarities among models, the performance of our speech recognizer-based spectral subtraction method could be further improved. Experimental results... 

Spectral Subtraction (SS), as a speech enhancement technique, originally designed for improving quality of speech signal judged by human listeners. it usually improve the quality and intelligibility of speech signals, while the speech recognition systems need compensation techniques capable of reducing the mismatch between the noisy speech features and the clean models. This paper proposes a novel approach for solving this problem by considering the SS and the speech recognizer as two interconnected components, sharing the common goal of improved speech recognition accuracy. The experimental evaluations on a real recorded database and the TIMIT database show that the proposed method can... 

Side Channel Analysis (SCA) are still harmful threats against security of embedded systems. Due to the fact that every kind of SCA attack or countermeasure against it needs to be implemented before evaluation, a huge amount of time and cost of this process is paid for providing high resolution measurement tools, calibrating them and also implementation of proposed design on ASIC or target platform. In this paper, we have introduced a novel simulation platform for evaluation of power based SCA attacks and countermeasures. We have used Synopsys power analysis tools in order to simulate a processor and implement a successful Differential Power Analysis (DPA) attack on it. Then we focused on the... 

In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is proposed which is an extension to the original 2DPCA. We state that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the covariance matrix of PCA. This implies that 2DPCA eliminates some covariance information that can be useful for recognition. E2DPCA instead of just using the main diagonal considers a radius of r diagonals around it and expands the averaging so as to include the covariance information within those diagonals. The parameter r unifies PCA and 2DPCA. r=1 produces the covariance of 2DPCA, r=n that of PCA. Hence, by controlling r it is possible to control the... 

In this paper we present a novel approach for adjusting a multi band spectral subtraction filter coefficients based on speech recognition system results. Currently most speech enhancement techniques are designed according to various waveform level criteria such as maximizing SNR or minimizing signal error. However improvement in these criteria does not necessarily result in increasing speech recognition performance. Only if these methods generate sequence of features that maximize or increase the likelihood of the correct transcription relative to other incorrect competing hypotheses, speech recognition performance will increase. Here we use an utterance with a known transcription and...