Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

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

Dual-tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram Method for Palmprint Recognition

In the paper, we improve the Local Binary Pattern Histogram (LBPH) approach and combine it with Dual-Tree Complex Wavelet Transform (DT-CWT) to propose a Dual-Tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram (DT-CWT based LBPWH) method for palmprint representation and recognition. The approximate shift invariant property of the DT-CWT and its good directional selectively in 2D make it a very appealing choice for palmprint representation. LBPH is a powerful texture description method, which considers both shape and texture information to represent an image. To enhance the representation capability of LBPH, a weight set is computed and assigned to the finial feature histogram. Here we needn't construct a palmprint model by a train sample set, which is not like some methods based on subspace discriminant analysis or statistical learning. In the approach, a palmprint image is first decomposed into multiple subbands by using DT-CWT. After that, each subband in complex wavelet domain is divided into non-overlapping sub-regions. Then LBPHs are extracted from each sub-region in each subband, and lastly, all of LBPHs are weighted and concatenated into a single feature histogram to effectively represent the palmprint image. A Chi square distance is used to measure the similarity of different feature histograms and the finial recognition is performed by the nearest neighborhood classifier. A group of optimal parameters is chosen by 20 verification tests on our palmprint database. In addition, the recognition results on our palmprint database and the database from the Hong Kong Polytechnic University show the proposed method outperforms other methods

Embedded Palmprint Recognition System Using OMAP 3530

We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance

Characterization of palmprints by wavelet signatures via directional context modeling

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

Efficent Approach Use to Increase accuracy Multimodal Biometric System for Feature Level Fusion

Security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process.In this paper we propose a Efficient and Robust Multimodal Biometric System for feature level fusion that combines the information to investigate whether the integration of fingerprints and signatures . Proposed system extracts Gabor texture from the preprocessed fingerprints and signatures sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore, we proposed wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.proposed approach is authenticated for their accuracy on Fingerprints virtual database fused with signature virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 99.8% and with false rejection rate (FRR) of = 1