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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,...
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...
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...
A fast and effective fade detection algorithm is proposed in this paper, which directly operates in compressed domain and suitable for real-time implementation. By analyzing the prediction directions of B frames, which are revealed in the macroblock types, the candidate fades can be found. Then, uncommon intracoded macroblocks of the P frame can be applied as an indicator of fade. As a result, locating...
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