Dynamic weighted discrimination power analysis: A novel approach for face and palmprint recognition in DCT domain

Although Discrete Cosine Transform (DCT) is widely employed to extract proper features for biometric recognition, the problem on how to select proper DCT coefficients to obtain the best discrimination effect has not been solved satisfactorily. Some approaches discard the low-frequency DCT coefficients unreasonably and rely on proper premasking window to improve performance. But there is not a uniform criterion to optimize the shape and size of the premasking window, so it is an inconvenient processing for coefficient selection. Three processes, used to enhance discriminant ability in DCT domain, and the relationship between them are summarized and discussed systematically. Furthermore, this paper explains the phenomenon why the recognition rate is low without discarding the lowfrequency DCT coefficients reasonably and then proposes dynamic weighted discrimination power analysis (DWDPA) to enhance the discrimination power (DP) of the selected DCT coefficients. DWDPA does not need premasking window and preserves more DCT coefficients with higher DP. Normalization prevents the DCT coefficients with large absolute values from destroying the DP of the other DCT coefficients that have less absolute values but high DP values. The DCT coefficients with larger DP values are given larger weights adaptively to optimize and enhance the recognition performance. The experiments on ORL, Yale and PolyU databases captured by biometric sensors prove the advantages of DWDPA obviously.