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It is a critical problem to protect the security and integrity of the biometric data for ensuring valid biometric identification. Recently, correlation analysis methods, making use of correlation between biometric images and cover images, become popular to protect biometric data. This paper proposes different correlation analysis algorithms. Optimally pruned extreme learning machine (OP-ELM) is the...
It is well known that extracting effective features from images is a crucial step for appearance-based face recognition methods. In this paper, an effective framework for extracting discriminant features, by so called Discriminant Class-dependence Feature Analysis (DCFA), which combines Linear Discriminant Analysis (LDA) and 1-D Class-dependence Feature Analysis (1D-CFA), is proposed. From one side,...
A relatively unexplored problem in facial expression analysis is how to select the positive and negative samples with which to train classifiers for expression recognition. Typically, for each action unit (AU) or other expression, the peak frames are selected as positive class and the negative samples are selected from other AUs. This approach suffers from at least two drawbacks. One, because many...
This paper presents a solution for video retrieval of frontal-view indoor moving pedestrians. A novel and effective system which contains two parts, feature extraction and key frame sets matching, is proposed. For the first part, a successful fusion strategy is proposed for effectively combining information from color and texture features. The experiment indicates that the retrieval accuracy based...
SENSC algorithm is a newly proposed stable and efficient NSC algorithm. In this paper the SENSC algorithm is evaluated for the task of image clustering. A series of experiments are conducted on two different kinds of image datasets, including face images and natural images, and SENSC is compared with some other commonly used clustering methods. Experimental results show that SENSC is better suited...
Non-negative Matrix Factorization (NMF) is a recently developed method for dimensionality reduction, feature extraction and data mining, etc. Currently no NMF algorithm holds both satisfactory efficiency for applications and enough ease of use. To improve the applicability of NMF, this paper proposes a new monotonic, fixed-point algorithm coined FastNMF by implementing least squares error-based non-negative...
In this paper, a novel class-dependence feature analysis method based on Correlation Filter Bank (CFB) technique for effective multimodal biometrics fusion at the feature level is developed. In CFB, the unconstrained correlation filter trained for a specific modality is designed by optimizing the overall original correlation outputs. Therefore, the differences between modalities have been taken into...
In this paper, one of the problems of linear discriminant analysis (LDA), that is, it pays more attention on minimizing the within-class scatter than on maximizing the between-class scatter, is treated. Though the weighted maximum margin criterion (WMMC) with an appropriate weighted coefficient can solve this problem, how to select this coefficient automatically is still difficult as most of previous...
In this paper, a new method for extracting expression-independent face features based on HOSVD (higher-order singular value decomposition) is proposed and used for face recognition. In the new method, it is assumed that a facial expression could be represented by the facial expressions in the training set. In addition, the expression with higher similarity to the expression of test person has higher...
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