A novel method of feature selection combined with sample selection is proposed to select discriminant features in this paper. Based on support vector machine trained on training set, the samples excluding the misclassified samples and support vector samples are used to select informative features during the procedure of recursive feature selection. The feature selection method is applied to sev...

This paper presents a hybrid approach to face-feature extraction based on the trace transform and the novel kernel partial-least-squares discriminant analysis (KPA). The hybrid approach, called trace kernel partial-least-squares discriminant analysis (TKPA) first uses a set of fifteen trace functionals to derive robust and discriminative facial features and then applies the KPA method to reduce...

In this study, four statistical classifiers, namely linear discriminant classifier, quadratic discriminant classifier, k-Nearest Neighborhood classifier, and parzen classifier are considered for recognition of 2D-shapes. The octagonal shape features are identified from 2D-shapes with the morphological shape decomposition technique. These features are reduced using principle component analysis. ...

Face expression recognition can be stated as „identifying the expression of an individual from images of the face‟. Most of the existing systems of facial expression recognition focus on gray scale image features. This paper describes the novel approaches for effectively recognizing the facial expressions. In facial expression recognition (FER) framework, initially the face region of the image ...

BACKGROUND The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. METHODS A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of inte...

Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporati...

Support vector machines (SVM’s) can be trained to classify manner transitions between phones and to identify the place of articulation of any given phone with high accuracy. The discriminant outputs of these SVM’s can be used as input features for a standard ASR system. There is a significant improvement in correctness and accuracy using these SVM discriminant features when compared to an MFCC ...

Support vector machines (SVMs) are trained to detect acoustic-phonetic landmarks, and to identify both the manner and place of articulation of the phones producing each landmark with high accuracy. The discriminant outputs of these SVMs are used as input features for a standard HMM based ASR system. There is a significant improvement in both the phone and word recognition accuracy when using th...

Face recognition is a challenging problem due to variations in pose, illumination, and expression. Techniques that can provide effective feature representation with enhanced discriminability are crucial. Wavelets have played an important role in image processing for its ability to capture localized spatial-frequency information of images. In this paper, we propose a novel local discriminant coo...

electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...