228 research outputs found
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
Iris Recognition Using Scattering Transform and Textural Features
Iris recognition has drawn a lot of attention since the mid-twentieth
century. Among all biometric features, iris is known to possess a rich set of
features. Different features have been used to perform iris recognition in the
past. In this paper, two powerful sets of features are introduced to be used
for iris recognition: scattering transform-based features and textural
features. PCA is also applied on the extracted features to reduce the
dimensionality of the feature vector while preserving most of the information
of its initial value. Minimum distance classifier is used to perform template
matching for each new test sample. The proposed scheme is tested on a
well-known iris database, and showed promising results with the best accuracy
rate of 99.2%
The fundamentals of unimodal palmprint authentication based on a biometric system: A review
Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases
Biometric recognition based on the texture along palmprint lines
Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201
Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition
Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %
Fourier spectral of PalmCode as descriptor for palmprint recognition
Study on automatic person recognition by palmprint is currently a hot topic. In this paper, we propose a novel palmprint recognition method by transforming the typical palmprint phase code feature into its Fourier frequency domain. The resulting real-valued Fourier spectral features are further processed by horizontal and vertical 2DPCA method, which proves highly efficient in terms of computational complexity, storage requirement and recognition accuracy. This paper also gives a contrast study on palm code and competitive code under the proposed feature extraction framework. Besides, experimental results on the Hongkong PolyU Palmprint database demonstrate that the proposed method outperforms many currently reported local Gabor pattern approaches for palmprint recognition
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