src-fusion.zip - 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. ,2013-03-14 16:40:42,下载52次