Abstract-This paper presents a family variable step-size (VSS) affine projection (AP) adaptive filtering algorithms with good convergence speed and low steady state mean square error features. In the following, the family of VSS selective partial updates (SPU) affine projection algorithms are established which reduce the computational complexity. The stability bounds of the family of APA and SPU-APA algorithms are analyzed based on the energy conservation arguments. This analysis does not need to assume a Gaussian or white distribution for the regressors. We demonstrate the good performance of the proposed algorithms through simulations in system identification and acoustic echo cancellation scenarios.Index Terms-Adaptive filter, affine projection, selective partial update, variable step-size, stability analysis.
In this paper the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection (AP) adaptive filtering algorithm are combined and the family of affine projection algorithms with SPU and SR features are established. These algorithms are computationally efficient. We demonstrate the performance of the presented algorithms through simulations.Index Terms-Adaptive filter, affine projection, selective partial update, selective regressor.
Face recognition applications focus on local features to prevent detailed information from being omitted while the feature extraction processes. This paper is based on presenting a local pattern-based model to extract more discriminative features that lead to more accurate classification. In local pattern-based feature extraction, the LBP is one of the most important approaches that many variants of this method have been proposed till now. LBP calculation is based on differences between the central pixel and the desired one. In contrast, the information hidden in the selected pixel's neighborhood pixels is not included in this process. This paper proposes the DR_LBP approach to address this failure by defining distances and using some of them in a ratio form. Successful results have been earned in many experimental results. In LBP, the calculations' primary flow takes advantage of two pixels in the LBP box, the central and the desired pixel. Contrary to the original LBP, this paper's proposed approach uses three pixels of LBP box to conduct the feature vector, which leads to employing the information hidden in the relationship between neighboring pixels. This approach applies the experiments on two standard datasets, ORL Yale face and Faces94 dataset. The accuracy percent of the proposed plan is 95.95, 94.09 and 98.01 on ORL, Yale face and Faces94 dataset, respectively, which is the reason to present this model as a new face feature extraction approach.
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