This paper presents a novel facial expression recognition methodology. In order to classify the expression of a test face to one of seven pre-determined facial expression classes, multiple two-class classification tasks are carried out. For each such task, a unique set of features is identified that is enhanced, in terms of its ability to help produce a proper separation between the two specifi...

The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the paradigm of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize t...

The efficient and discriminating feature extraction is a significant problem in pattern recognition and computer vision. This paper presents a novel Discriminating Haar (D-Haar) features for eye detection. The DHaar feature extraction starts with a Principal Component Analysis (PCA) followed by a whitening transformation on the Haar feature space. A discriminant analysis is then performed on th...

We study three aspects of designing appearance based visual features for automatic lipreading: (a) The choice of the video region of interest (ROI), on which image transform features are obtained; (b) The extraction of speech discriminant features at each frame; and (c) The use of temporal information to improve visual speech modeling. In particular, with respect to (a), we propose a ROI that i...

Users are still waiting for accurate optical character recognition solutions for Arabic handwritten scripts. This research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic letters. Depending on their position in the word, Arabic letters are drawn in four forms: Isolated, Initial, Medial, and Final. The principal component analysis...

We have recently proposed a new acoustic model based on probabilistic linear discriminant analysis (PLDA) which enjoys the flexibility of using higher dimensional acoustic features, and is more capable to capture the intra-frame feature correlations. In this paper, we investigate the use of bottleneck features obtained from a deep neural network (DNN) for the PLDA-based acoustic model. Experime...

Feature combination techniques based on PCA, LDA and HLDA are compared in experiments where limited amount of training data is available. Success with feature combination can be quite dependent on proper estimation of statistics required by the used technique. Insufficiency of training data is, therefore, an important problem, which has to be taken in to account in our experiments. Besides of s...

This paper investigates the performance and relevance of a set of acoustic features for the task of automatic recognition of affect from speech using machine learning techniques. Eighty seven novel and classical features related to loudness, intonation, and voice quality, are examined. Using feature selection, the results yield a performance level of 49.4% recognition rate (compared to a human ...

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission COVID-19. However, achieving accurate facial detection while people wear or similar occlusions is major challenge. This paper introduces model detect occluded masked faces based on fused convolutional graphs. includes deep neural architecture with two spatial-based graphs that rely set key fea...