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Emotion recognition has been of great interest in psychology, machine intelligence, human–machine interaction and biomedical fields. This paper proposes a novel soft computing technique for facial emotion recognition by introducing edge- enhanced bidimensional empirical mode decomposition (EEBEMD) as a feature extraction tool for facial emotion recognition. Facial images are subjected to optimized...
The aim of this work is to automatically detect and analyse the emotions from the digital videos and images. Initially the images are extracted from pre-recorded videos, from which the faces are cropped automatically. The training dataset is formed with minimal number of images per subject for each emotion. Bi-dimensional Empirical Mode Decomposition (BEMD) is used to decompose the images in its Intrinsic...
This paper proposes a multilayer decomposition aided method based on textural and color feature for detection and classification of skin cancer images. Firstly, images are decomposed into a piecewise base layer and detail layer by weighted least squares (WLS) framework based edge-preserving decomposition. From detail or enhanced layer of original image, normalized symmetrical Grey Level Co-occurrence...
In this work, a method is proposed for classification of texture images using a fusion of feature sets. Weighted guided filter based preprocessing technique has been performed using optimized cost function to enhance the discriminative property of different texture images. A hybrid model of normalized symmetrical gray level co-occurrence matrix parameters, histogram of oriented gradients, and Gabor...
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