On the computation of distance between two linear discriminant models


On the computation of distance between two linear discriminant models – A novel approach for discriminematization based on distance is presented. One is presented in the form of a graph, and the other consists of a set of points as well as the weights of the two models. The resulting inference process is a two-stage procedure (the first stage takes the model-space and the second one takes the model-space and the weights of the two models, and uses them as a part of a multi-model learning system), which consists of either a discriminematizer or a discriminantizer. The discriminator performs discriminative inference from the graph using the similarity between the models. The discriminator’s results indicate that the discriminator has good performance and can be successfully applied to many applications.

In this paper, we propose a novel algorithm for the problem of classification of human faces from various facial expressions, using facial expressions in different video frames. The proposed method relies on a non-linear estimation of two sets of facial expressions by learning a matrix representation from the videos of face images. The proposed algorithm is applied to face databases where different facial expressions are available for each video frame. These databases are called databases of face images. The results obtained show that the proposed algorithm is successful in learning the representation of facial expressions. The algorithm is applied to face databases with more than 4,000 face images. The best results obtained by the proposed algorithm have been obtained in the database of human faces at different frame numbers.

Predicting outcomes through neural networks

Complexity-Aware Image Adjustment Using a Convolutional Neural Network with LSTM for RGB-based Action Recognition

On the computation of distance between two linear discriminant models

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  • Adversarial Learning for Brain-Computer Interfacing: A Survey

    Classification with Asymmetric Leader SelectionIn this paper, we propose a novel algorithm for the problem of classification of human faces from various facial expressions, using facial expressions in different video frames. The proposed method relies on a non-linear estimation of two sets of facial expressions by learning a matrix representation from the videos of face images. The proposed algorithm is applied to face databases where different facial expressions are available for each video frame. These databases are called databases of face images. The results obtained show that the proposed algorithm is successful in learning the representation of facial expressions. The algorithm is applied to face databases with more than 4,000 face images. The best results obtained by the proposed algorithm have been obtained in the database of human faces at different frame numbers.


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