文件名称:src-fusion
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2013-03-14
- 文件大小：
- 32kb
- 下载次数：
- 0次
- 提 供 者：
- abdel******
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A. Fusion at the Feature Extraction Level
The data obtained from each sensor is used to compute a
feature vector. As the features extracted from one biometric
trait are independent of those extracted from the other, it is
reasonable to concatenate the two vectors into a single new
vector. The primary benefit of feature level fusion is the
detection of correlated feature values generated by different
feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy
[14]. The new vector has a higher dimension and represents the
identity of the person in a different hyperspace. Eliciting this
feature set typically requires the use of dimensionality
reduction/selection methods and, therefore, feature level fusion
assumes the availability of a large number of training data.-A. Fusion at the Feature Extraction Level
The data obtained from each sensor is used to compute a
feature vector. As the features extracted from one biometric
trait are independent of those extracted from the other, it is
reasonable to concatenate the two vectors into a single new
vector. The primary benefit of feature level fusion is the
detection of correlated feature values generated by different
feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy
[14]. The new vector has a higher dimension and represents the
identity of the person in a different hyperspace. Eliciting this
feature set typically requires the use of dimensionality
reduction/selection methods and, therefore, feature level fusion
assumes the availability of a large number of training data.
The data obtained from each sensor is used to compute a
feature vector. As the features extracted from one biometric
trait are independent of those extracted from the other, it is
reasonable to concatenate the two vectors into a single new
vector. The primary benefit of feature level fusion is the
detection of correlated feature values generated by different
feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy
[14]. The new vector has a higher dimension and represents the
identity of the person in a different hyperspace. Eliciting this
feature set typically requires the use of dimensionality
reduction/selection methods and, therefore, feature level fusion
assumes the availability of a large number of training data.-A. Fusion at the Feature Extraction Level
The data obtained from each sensor is used to compute a
feature vector. As the features extracted from one biometric
trait are independent of those extracted from the other, it is
reasonable to concatenate the two vectors into a single new
vector. The primary benefit of feature level fusion is the
detection of correlated feature values generated by different
feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy
[14]. The new vector has a higher dimension and represents the
identity of the person in a different hyperspace. Eliciting this
feature set typically requires the use of dimensionality
reduction/selection methods and, therefore, feature level fusion
assumes the availability of a large number of training data.
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下载文件列表
mLib
....\cal_mu.m
....\cal_sigma.m
....\cal_weight_brute.m
....\cal_weight_fisher.m
....\draw_empiric.m
....\draw_theory.m
....\epc.m
....\Fratio_norm.m
....\f_eer.m
....\f_ratio.m
....\f_ratio_wsum.m
....\gaussianity_test.m
....\hter.m
....\hter_apriori.m
....\hter_significant_plot.m
....\hter_significant_test.m
....\hter_significant_test_new.m
....\load_raw_scores.m
....\load_raw_scores_labels.m
....\Make_DET.m
....\normalise_scores.m
....\ppndf.m
....\sigmoid_inv.m
....\spectro.m
....\subset.m
....\VR_analysis.m
....\VR_draw.m
....\VR_Fnorm.m
....\VR_normalisation.m
....\VR_normalisation_old.m
....\wer.asv
....\wer.m
....\wer_apriori.m
mScripts
........\config.m
........\epc_global.m
........\fusion_method.m
........\fusion_wsum.m
........\fusion_wsum_brute.m
........\initialise.m
........\main_fusion.asv
........\main_fusion.m
........\main_fusion.pdf
........\main_tutorials.asv
........\main_tutorials.m
........\plot_all_epc.m
........\test_method.m
........\train_method.m