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.