Combining Fisher locality preserving projections and passband DCT for efficient palmprint recognition

• Propose a ROI method to extract complete palm׳s area to capture all palm information.
• Investigate palmprint feature extraction using Passband DCT (PDCT).
• Show that PDCT is an efficient dimensionality reduction in the presence of degradations.
• Propose a new linear subspace technique to maximize between-class scatter.
• Propose a new linear subspace method to compact effectively the within-class scatter.

In this paper a new graph based approach referred to as Fisher Locality Preserving Projections (FLPP) is proposed for efficient palmprint recognition. The technique employs two graphs with the first being used to characterize the within-class compactness and the second being dedicated to the augmentation of the between-class separability. In addition, a Passband Discrete Cosine Transform (PBDCT) is used for dimensionality reduction and feature extraction. This process makes the palmprint features more robust against inherent degradations of palmprint images. By applying an FLPP, only the most discriminant and stable palmprint features are retained. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area one should carefully consider this fact when performing the feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows the efficient extraction of the whole palm area ignoring all the undesirable parts, such as the fingers and background. The experimental results demonstrate the effectiveness of the proposed method even for highly degraded palmprint images. An Equal Error Rate (EER) of 0.48% has been obtained on a database of 4000 palmprint images.