To enhance the recognition performance of the biometric system,
this section suggests two advanced biometric recognition technologies,
biometric data discrimination and multi-biometric technologies. In biometric
data discrimination, we first introduce the fundamental of biometric data
discrimination, and then suggest using a family of tensor discriminate analysis
to deal with the diversity in forms of biometric data. In multi-biometrics, we
introduce three categories of fusion strategies to enhance the performance and
reliability of the biometric system.
Besides recognition performance, security and privacy
issues should also be taken in account. In terms of security, there are many
attacks, such as overplay, database and brute-force attacks, on biometric
applications. In terms of privacy, biometric traits may carry additional
sensitive personal information. For example, genetic disorders might be
inferred from the DNA data used for personal identification.
Linear discriminates analysis (LDA) method has been widely studied
in and successfully applied to biometric recognition such as face, fingerprint, and palm print identification
or verification.
The essence of LDA is to construct a linear discriminates
criterion which can be used to build a binary classifier or a feature
extractor. To differentiate LDA for binary classification from LDA for feature
extraction, hereafter we name the former as classification-oriented LDA, and
the later feature extraction-oriented LDA.
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