Table 2 - uploaded by Mohsen Ardabilian
Content may be subject to copyright.
Source publication
In this paper, we consider the problem of feature selection and classifier fusion and discuss how they should be reflected in the fusion system architecture. We employed the genetic algorithm with a novel coding to search the worst performing fusion strategy. The proposed algorithm tunes itself between feature and matching score levels, and improve...
Context in source publication
Context 1
... this case, the final recognition rate is 97.27% using Neutral vs All experiment. Table 2 compares the proposed fusion strategy with other fusion approaches (simple sum rule (baseline method), Gökberk and al. [7], Mian and al. [2]). The performance of each feature and classifiers selected by the GA is displayed in Table1. ...
Similar publications
This paper solves the multi-class classification problem for Parkinson's disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework uti...
One of the most challenges in the remote sensing applications is Hyperspectral image classification. Hyperspectral image classification accuracy depends on the number of classes, training samples and features space dimension. The classification performance degrades to increase the number of classes and reduce the number of training samples. The inc...
Recently, there are increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works usually used spatiotemporal local binary pattern for micro-expression...
One of the important roles for internet traffic classification is feature selection method. This method will present more accurate data and more accurate internet traffic classification which will provide precise information for bandwidth optimization. One of the important considerations in the feature selection method that should be looked into is...
The output of feature selection is normally at least an optimal (in the sense of maximally discriminating) feature subset. In exhaustive search, not only the best subset but all the feature subsets of a given cardinality are ranked. From this feature subsets ranking it is possible to extract a very informative ranking of single features. This we do...
Citations
Face recognition (FR) was one of the motivations of computer vision for a long time, but only in recent years reliable automatic face recognition has become a realistic target of biometrics research. This interest is motivated by several reasons. First, the face is one of the most preferable biometrics for person identification and verification related applications, because it is natural, non-intrusive, and socially well accepted. The second reason relates to the challenges encountered in the FR domain, in which all human faces are similar to each other and hence offer low distinctiveness as compared with other biometrics, e.g., fingerprints and irises. Furthermore, when employing facial texture images, intra-class variations due to various factors as illumination and pose changes are usually greater than inter-class ones, preventing 2D face recognition systems from being completely reliable in real conditions.Recent, 3D acquisition systems are capable to capture the shape information of objects. Thus, 3D face recognition (3D FR) has been extensively investigated by the research community to deal with the unsolved issues in 2D face recognition, i.e., illumination and pose changes. Indeed, 3D cameras generally deliver the 3D scans of faces with their aligned texture images. 3D FR can benefit from the fusion of 2D texture and 3D shape information.This Ph.D thesis is dedicated to the optimization of fusion strategies based on three dimensional data. However, there are some problems. Indeed, since the 3D face scans provide both the facial surfaces for the 3D model and 2D texture images, the number of fusion method is high.In the literature, many fusion strategies exist that have been proposed for 3D face recognition. We can roughly classify the fusion strategies into two categories: early fusion and late fusion. Some intermediate strategies such as serial fusion and multi-level fusion have been proposed as well. Meanwhile, the search for an optimal fusion scheme remains extraordinarily complex because the cardinality of the space of possible fusion strategies. It is exponentially proportional to the number of competing features and classifiers. Thus, we require fusion technique to efficiently manage all these features and classifiers that constitute our contribution in this work. In addition, the optimality criteria of fusion strategies remain critical issues. By definition, an optimal fusion strategy is able to integrate and take advantage from different data.To overcome all these difficulties and propose an optimized solution, we adopted the following reflection. [...]
