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

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

Partial palmprint matching using invariant local minutiae descriptors

In forensic investigations, it is common for forensic investigators to obtain a photograph of evidence left at the scene of crimes to aid them catch the culprit(s). Although, fingerprints are the most popular evidence that can be used, scene of crime officers claim that more than 30% of the evidence recovered from crime scenes originate from palms. Usually, palmprints evidence left at crime scenes are partial since very rarely full palmprints are obtained. In particular, partial palmprints do not exhibit a structured shape and often do not contain a reference point that can be used for their alignment to achieve efficient matching. This makes conventional matching methods based on alignment and minutiae pairing, as used in fingerprint recognition, to fail in partial palmprint recognition problems. In this paper a new partial-to-full palmprint recognition based on invariant minutiae descriptors is proposed where the partial palmprint’s minutiae are extracted and considered as the distinctive and discriminating features for each palmprint image. This is achieved by assigning to each minutiae a feature descriptor formed using the values of all the orientation histograms of the minutiae at hand. This allows for the descriptors to be rotation invariant and as such do not require any image alignment at the matching stage. The results obtained show that the proposed technique yields a recognition rate of 99.2%. The solution does give a high confidence to the judicial jury in their deliberations and decision

A sermon preached before the Lord Mayor and the Court of Aldermen at Guild-Hall Chappel on the 7th of May 1682 / by Francis Turner ...

In this paper, we propose a solution for the problem of rotated partial shoeprint retrieval, based on the combined use of local points of interest and SIFT descriptor. Once the generated features are encoded using SIFT descriptor, matching is carried out using RANSAC to estimate a transformation model and establish the number of its inliers which is then multiplied by the sum of point-topoint Euclidean distances below a hard threshold. We demonstrate that such combination can overcome the issue of retrieval of partial prints in the presence of rotation and noise distortions. Conducted experiments have shown that the proposed solution achieves very good matching results and outperforms similar work in the literature both in terms of performance and complexity