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Feature pooling layers (e.g., max pooling) in convolutional neural networks (CNNs) serve the dual purpose of providing increasingly abstract representations as well as yielding computational savings in subsequent convolutional layers. We view the pooling operation in CNNs as a two step procedure: first, a pooling window (e.g., 2× 2) slides over the feature map with stride one which leaves...
A feature selection method based on intelligent dynamic swarm and fuzzy rough set is proposed in this paper, in which the fitness function is dependency measure. The proposed method can identify irrelevant and redundant features, and after dropping them produces the reduced feature set. Results found by this method have been compared with the results obtained using another established methods of particle...
In this paper, we introduce new methods to encode color local texture features for enhanced face representation. In particular, we first propose a novel descriptor; color local phase quantization (CLPQ), which incorporates (channel-wise) unichrome and (cross channel) opponent features in frequency domain. Furthermore, we extend the CLPQ descriptor to multiple scales i.e. multiscale color LPQ (MS-CLPQ),...
This paper shows a work done under Affective Computing umbrella and in the field of emotion recognition. The paper explores the anatomy of a human face and builds the classification model based on it. The anatomical information of face is used to locate several points on the face and to extract the features. The features are in form of distance vectors which can be of specific person or group of persons...
In this paper we proposed a novel multimodal biometric approach using iris and periocular biometrics to improve the performance of iris recognition in case of non-ideal iris images. Though iris recognition has the highest accuracy among all the available biometrics, still the noises at the image acquisition stage degrade the recognition accuracy. The periocular region can act as a supporting biometric,...
This paper presents a general multi-view feature extraction approach that we call Generalized Multiview Analysis or GMA. GMA has all the desirable properties required for cross-view classification and retrieval: it is supervised, it allows generalization to unseen classes, it is multi-view and kernelizable, it affords an efficient eigenvalue based solution and is applicable to any domain. GMA exploits...
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