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Eye detection plays an important role in many fields, because eyes provide prominent facial feature information. However, changes in face pose, illumination variation, with glasses, and eye occlusions can make it difficult to detect eyes well from facial images. This paper proposes a hybrid model for eye detection. The model is an integration of two classifiers: Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). In order to improve the speed of detection in the system, an eye variance filter (EVF) is constructed for eliminating most of noneye images to keep less candidate eye images. The CNN then works as a trainable feature extractor to explicitly extract various latent eye features. Finally, the trained SVM classifier is employed for eye verification instead of using the CNN classification function. Experiments applying the model have been conducted on the BioID, IMM, FERET and ORL face databases. Comparisons with other methods on the same databases indicate that this hybrid model has achieved a higher detection accuracy. Extensive experiments demonstrate the robustness and efficiency of our method by testing it on different facial images with varying eye conditions. [ABSTRACT FROM AUTHOR]

A novel supervised particle swarm optimization (S-PSO) classification algorithm is proposed for fault diagnosis. In order to improve the accuracy of fault diagnosis and obtain the global optimal solutions with a higher probability, two strategies, i.e. a hybrid particle position updating strategy and a fixed iteration interval intervention updating strategy, are designed to balance the effect of the local and the global search. These methods increase the diversity of particles, expand the particles ability of searching the entire solution space, and guide the particles adaptively jumping out of the local optimal area. Meanwhile, based on the shorter intra-class distance, longer inter-class distance and maximum classification accuracy of training samples, a fitness function is designed to constraint the output optimal class centers. Experimental results demonstrate that the proposed S-PSO classification algorithm can overcome the problems in the classical clustering algorithms, which only consider the similarity of data instead of their physical meanings. The comparison on GE90 engine borescope image texture feature classification is also conducted. The results show that the performance of S-PSO classification algorithm is robust. Its classification accuracy is higher than those of popular methods, including support vector machine (SVM), neural network, Bayesian classifier, and k-nearest neighbor (k-NN) algorithm. [ABSTRACT FROM AUTHOR]

This paper offers a recurrent neural network to support vector machine (SVM) learning in regression arising widespread applications in a variety of setting. The SVM learning problem in regression is first converted into an equivalent quadratic programming (QP) formulation. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees to obtain the optimal solution of the support vector regression (SVR). The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. Two illustrative examples provide a further demonstration of the effectiveness of the method. [ABSTRACT FROM AUTHOR]

One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its extension for multi-class implementation offers an open scheme which allows easily adding new classes. However, using OCCs for the multi-class implementation usually achieves less accuracy than the usual multi-class implementations. Hence, in order to improve the accuracy and keep the offered advantage, the suitable approach consists to combine different classifiers. Thus, this paper proposes a study of combining different types of OCC for multi-class classification by means of a new Dynamic Weighted Average (DWA) combination rule. Experimental results conducted on several real-world datasets prove the effective use of the proposed approach where the DWA rule achieves the best results against fixed rules as well as the decision template. Furthermore, comparison of the proposed open classification system against a standard open classifier based on K-Nearest Neighbor (K-NN) shows the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

Fingerprint classification reduces the searching time of an automated fingerprint identification system. Since fingerprints have properties of intra-class diversities and inter-class similarities, the ambiguous example causes a difficult problem in the fingerprint classification. In order to address the problem, we have analyzed fingerprints' subclasses with multiple decision templates. It clusters the soft outputs of support vector machines (SVMs) into several sub-classes using the self-organizing maps, and estimates a localized template for each sub-class. For an input fingerprint, the proposed method matches the output vector of SVMs to each template and finally categorizes the sample into the class of the most similar template. Experimental results on the FingerCode dataset demonstrate the effectiveness of the subclass-based approach compared with previous methods. [ABSTRACT FROM AUTHOR]