Biometric association using transfer subspace learning

Abstract

Different biometrics studies related to face, fingerprint, hand geometry, palmprint, and iris has been taken place in recent past. In this paper two biometrics, face and fingerprint are considered and associated with each other using transfer subspace learning. Transfer subspace learning can share common subspace even if domains are different by minimizing the distance between their probability distribution. Locality preserving projections is used as objective function to discriminate between source domains. This works linearly and preserves the local geometry of the structure by obtaining the subspace spanned by smallest eigenvectors of local covariance matrix. Proposed framework will be very useful in the forensic applications. Though the accuracy of this system is not very high it will help to separate the most probable suspects.