The development of automated palmprint identification using major flexion creases

Palmar flexion crease matching is a method for verifying or establishing identity. New methods of palmprint identification, that complement existing identification strategies, or reduce analysis and comparison times, will benefit palmprint identification communities worldwide. To this end, this thesis describes new methods of manual and automated palmar flexion crease identification, that can be used to identify palmar flexion creases in online palmprint images. In the first instance, a manual palmar flexion crease identification and matching method is described, which was used to compare palmar flexion creases from 100 palms, each modified 10 times to mimic some of the types of alterations that can be found in crime scene palmar marks. From these comparisons, using manual palmar flexion crease identification, results showed that when labelled within 10 pixels, or 3.5 mm, of the palmar flexion crease, a palmprint image can be identified with a 99.2% genuine acceptance rate and a 0% false acceptance rate. Furthermore, in the second instance, a new method of automated palmar flexion crease recognition, that can be used to identify palmar flexion creases in online palmprint images, is described. A modified internal image seams algorithm was used to extract the flexion creases, and a matching algorithm, based on kd-tree nearest neighbour searching, was used to calculate the similarity between them. Results showed that in 1000 palmprint images from 100 palms, when compared to manually identified palmar flexion creases, a 100% genuine acceptance rate was achieved with a 0.0045% false acceptance rate. Finally, to determine if automated palmar flexion crease recognition can be used as an effective method of palmprint identification, palmar flexion creases from two online palmprint image data sets, containing images from 100 palms and 386 palms respectively, were automatically extracted and compared. In the first data set, that is, for images from 100 palms, an equal error rate of 0.3% was achieved. In the second data set, that is, for images from 386 palms, an equal error rate of 0.415% was achieved.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multispectral Palmprint Recognition Using Textural Features

In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features and used them for palmprint recognition. Co-occurrence matrix can be used for textural feature extraction. As classifiers, we have used the minimum distance classifier (MDC) and the weighted majority voting system (WMV). The proposed method is tested on a well-known multispectral palmprint dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most scenarios which outperforms all previous works in multispectral palmprint recognition.Comment: 5 pages, Published in IEEE Signal Processing in Medicine and Biology Symposium 201

Characterization of palmprints by wavelet signatures via directional context modeling

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2019, Hyderabad, Indi

Advanced Partial Palmprint Matching Based on Repeated Adjoining Minutiae

Nowadays, high resolution palmprint images are used for recognition. The features that can be extracted from a high resolution palmprint image include the minutiae points. In this paper, instead of full palmprints, partial palmprints are used for matching. Partial refers to a part of the palmprint such as the thenar and hypothenar or hypothenar and interdigital areas. The minutiae can be easily located from the thinned palmprint image by using a window. Since there are a large number of minutiae present within a palmprint image, the minutiae are grouped into several clusters. The extracted minutiae are clustered using Hough circles. In order to avoid spurious minutiae resulting from the presence of immutable creases, radon transform is made use of. By selecting initial minutiae pairs, the entire matching is done by using repeated adjoining minutiae matching. The algorithm is developed and successfully tested with palmprint database. DOI: 10.17762/ijritcc2321-8169.15017

Fast and efficient palmprint identification of a small sample within a full image.

In some fields like forensic research, experts demand that a found sample of an individual can be matched with its full counterpart contained in a database. The found sample may present several characteristics that make this matching more difficult to perform, such as distortion and, most importantly, a very small size. Several solutions have been presented intending to solve this problem, however, big computational effort is required or low recognition rate is obtained. In this paper, we present a fast, simple, and efficient method to relate a small sample of a partial palmprint to a full one using elemental optimization processes and a voting mechanic. Experimentation shows that our method performs with a higher recognition rate than the state of the art method, when trying to identify palmprint samples with a radius as small as 2.64 cm