Current Issue : October - December Volume : 2012 Issue Number : 4 Articles : 5 Articles
How to improve performance of an automatic fingerprint verification system (AFVS) is always a big challenge in\r\nbiometric verification field. Recently, it becomes popular to improve the performance of AFVS using ensemble\r\nlearning approach to fuse related information of fingerprints. In this article, we propose a novel framework of\r\nfingerprint verification which is based on the multitemplate ensemble method. This framework is consisted of\r\nthree stages. In the first stage, enrollment stage, we adopt an effective template selection method to select those\r\nfingerprints which best represent a finger, and then, a polyhedron is created by the matching results of multiple\r\ntemplate fingerprints and a virtual centroid of the polyhedron is given. In the second stage, verification stage, we\r\nmeasure the distance between the centroid of the polyhedron and a query image. In the final stage, a fusion rule\r\nis used to choose a proper distance from a distance set. The experimental results on the FVC2004 database prove\r\nthe improvement on the effectiveness of the new framework in fingerprint verification. With a minutiae-based\r\nmatching method, the average EER of four databases in FVC2004 drops from 10.85 to 0.88, and with a ridge-based\r\nmatching method, the average EER of these four databases also decreases from 14.58 to 2.51....
This article presents a non-data-aided adaptive symbol timing offset correction algorithm to enhance the\r\nequalization performance in the presence of long delay spread multipath channel. The optimal timing phase offset\r\nin the presence of multipath channels is the one jointly optimized with the receiver equalizer. The jointly\r\noptimized timing phase offset with a given fixed length equalizer should produce a discrete time channel\r\nresponse for which the equalizer achieves the minimum mean squared error among other discrete time channel\r\nresponses sampled by different timing phases. We propose a blind adaptive baseband timing recovery algorithm\r\nproducing a timing offset close to the jointly optimal timing phase compared to other existing non-data-aided\r\ntiming recovery methods. The proposed algorithm operates independently from the equalizer with the same\r\ncomputational complexity as the widely used Gardner timing recovery algorithm. Simulation results show that the\r\nproposed timing recovery method can result in considerable enhancement of equalization performances...
The iterative adaptive approach (IAA) can achieve accurate source localization with single snapshot, and therefore\r\nit has attracted significant interest in various applications. In the original IAA, the optimal filter is performed for\r\nevery scanning angle grid in each iteration, which may cause the slow convergence and disturb the spatial\r\nestimates on the impinging angles of sources. In this article, we propose an efficient implementation of IAA (EIAA)\r\nby modifying the use of the optimal filtering, i.e., in each iteration of EIAA, the optimal filter is only utilized to\r\nestimate the spatial components likely corresponding to the impinging angles of sources, and other spatial\r\ncomponents corresponding to the noise are updated by the simple correlation of the basis matrix with the\r\nresidue. Simulation results show that, in comparison with IAA, EIAA has significant higher computational efficiency\r\nand comparable accuracy of source angle and power estimation...
A blind dereverberation method based on power spectral subtraction (SS) using a multi-channel least mean\r\nsquares algorithm was previously proposed to suppress the reverberant speech without additive noise. The results\r\nof isolated word speech recognition experiments showed that this method achieved significant improvements\r\nover conventional cepstral mean normalization (CMN) in a reverberant environment. In this paper, we propose a\r\nblind dereverberation method based on generalized spectral subtraction (GSS), which has been shown to be\r\neffective for noise reduction, instead of power SS. Furthermore, we extend the missing feature theory (MFT), which\r\nwas initially proposed to enhance the robustness of additive noise, to dereverberation. A one-stage\r\ndereverberation and denoising method based on GSS is presented to simultaneously suppress both the additive\r\nnoise and nonstationary multiplicative noise (reverberation). The proposed dereverberation method based on GSS\r\nwith MFT is evaluated on a large vocabulary continuous speech recognition task. When the additive noise was\r\nabsent, the dereverberation method based on GSS with MFT using only 2 microphones achieves a relative word\r\nerror reduction rate of 11.4 and 32.6% compared to the dereverberation method based on power SS and the\r\nconventional CMN, respectively. For the reverberant and noisy speech, the dereverberation and denoising method\r\nbased on GSS achieves a relative word error reduction rate of 12.8% compared to the conventional CMN with\r\nGSS-based additive noise reduction method. We also analyze the effective factors of the compensation parameter\r\nestimation for the dereverberation method based on SS, such as the number of channels (the number of\r\nmicrophones), the length of reverberation to be suppressed, and the length of the utterance used for parameter\r\nestimation. The experimental results showed that the SS-based method is robust in a variety of reverberant\r\nenvironments for both isolated and continuous speech recognition and under various parameter estimation\r\nconditions....
We propose a new multi-target tracking approach, which is able to reliably track multiple objects even with poor\nsegmentation results due to noisy environments. The approach takes advantage of a new dual object model\ncombining 2D and 3D features through reliability measures. In order to obtain these 3D features, a new classifier\nassociates an object class label to each moving region (e.g. person, vehicle), a parallelepiped model and visual\nreliability measures of its attributes. These reliability measures allow to properly weight the contribution of noisy,\nerroneous or false data in order to better maintain the integrity of the object dynamics model. Then, a new multitarget\ntracking algorithm uses these object descriptions to generate tracking hypotheses about the objects moving\nin the scene. This tracking approach is able to manage many-to-many visual target correspondences. For achieving\nthis characteristic, the algorithm takes advantage of 3D models for merging dissociated visual evidence (moving\nregions) potentially corresponding to the same real object, according to previously obtained information. The\ntracking approach has been validated using video surveillance benchmarks publicly accessible. The obtained\nperformance is real time and the results are competitive compared with other tracking algorithms, with minimal\n(or null) reconfiguration effort between different videos...
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