New Face Representation Using Compressive Sensing

In this paper we present a new descriptor for representing face images. We used compressive sensing concept to prepare a Gaussian Random or Binary Random Measurement Matrix (GRMM). We simply project the face images to new space using GRMM. Classification is then performed using nearest neighbor classifiers. System performance is very promising and comparable with the well-known algorithms in the literature

A MONOGENIC LOCAL GABOR BINARY PATTERN FOR FACIAL EXPRESSION RECOGNITION

The paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on a local Gabor Pattern (LGP). The proposed algorithm is applied at different scales of the Gabor kernel with different normalization schemes. Results from the two best performing normalization algorithms with mono-LBP are fused at score level to obtain an improved performance. Moreover, performance comparison is done with other variants of LGP algorithm and also the effects of various normalization techniques are investigated. Experimental results on JAFFE facial expression database show that the new technique has the best average performance compared to its counterparts using distance metrics as a classifier.

Discrete-wavelet-transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix

The recursive-least-squares (RLS) algorithm was introduced as an alternative to LMS algorithm with enhanced performance. Computational complexity and instability in updating the autocolleltion matrix are some of the drawbacks of the RLS algorithm that were among the reasons for the intrduction of the second-order recursive inverse (RI) adaptive algorithm. The 2nd order RI adaptive algorithm suffered from low convergence rate in certain scenarios that required a relatively small initial step-size. In this paper, we propose a newsecond-order RI algorithm that projects the input signal to a new domain namely discrete-wavelet-transform (DWT) as pre step before performing the algorithm. This transformation overcomes the low convergence rate of the second-order RI algorithm by reducing the self-correlation of the input signal in the mentioned scenatios. Expeirments are conducted using the noise cancellation setting. The performance of the proposed algorithm is compared to those of the RI, original second-order RI and RLS algorithms in different Gaussian and impulsive noise environments. Simulations demonstrate the superiority of the proposed algorithm in terms of convergence rate comparedto those algorithms

Discrete wavelet transform-based RI adaptive algorithm for system identification

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions

Comparative Study on Facial Expression Recognition using Gabor and Dual-Tree Complex Wavelet Transforms

Moving from manually interaction with machines to automated systems, stressed on the importance of facial expression recognition for human computer interaction (HCI). In this article, an investigation and comparative study about the use of complex wavelet transforms for Facial Expression Recognition (FER) problem was conducted. Two complex wavelets were used as feature extractors; Gabor wavelets transform (GWT) and dual-tree complex wavelets transform (DT-CWT). Extracted feature vectors were fed to principal component analysis (PCA) or local binary patterns (LBP). Extensive experiments were carried out using three different databases, namely; JAFFE, CK and MUFE databases. For evaluation of the performance of the system, k-nearest neighbor (kNN), neural networks (NN) and support vector machines (SVM) classifiers were implemented. The obtained results show that the complex wavelet transform together with sophisticated classifiers can serve as a powerful tool for facial expression recognition problem

Face recognition using ensemble statistical local descriptors

The use of data fusion can be of a enormous help in boosting classification performance. Feature fusion is a data fusion technique that is being considered in this study. The effect of fusing different feature descriptors extracted by using histogram-based local feature extraction algorithms on the performance of the face recognition problem is investigated. Feature fusion/concatenation of more than one generated feature descriptor is applied. The impact of fused two and three feature descriptors on the system performance is evaluated when the training set is limited to only one-shot per person. Extensive experiments are carried out using two well-known face databases. Comparisons are conducted among different algorithms for extraction of the local statistical feature descriptors of the face images. The obtained results show that feature fusion of the descriptors can significantly improve the performance with certain feature descriptors

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Cardiovascular diseases (CVDs) like arrhythmia and heart failure remain the world’s leading cause of death. These conditions can be triggered by high blood pressure, diabetes, and simply the passage of time. The early detection of these heart issues, despite substantial advancements in artificial intelligence (AI) and technology, is still a significant challenge. This research addresses this hurdle by developing a deep-learning-based system that is capable of predicting arrhythmias and heart failure from abnormalities in electrocardiogram (ECG) signals. The system leverages a model that combines long short-term memory (LSTM) networks with convolutional neural networks (CNNs). Extensive experiments were conducted using ECG data from both the MIT-BIH and BIDMC databases under two scenarios. The first scenario employed data from five distinct ECG classes, while the second focused on classifying data from three classes. The results from both scenarios demonstrated that the proposed deep-learning-based classification approach outperformed existing methods