LettersPalmprint recognition using Gabor-based local invariant features
Introduction
Recently, there has been an extensive research on palmprint recognition owing to its distinguished characteristics including stable structures, low-cost and low-intrusiveness [1]. Early studies focus on structural features for off-line palmprint images of high resolution (up to 500 dpi) [2], [3]. As for the online palmprint images (less than 100 dpi) employed in most cases today, texture analysis has been introduced to palmprint recognition [4], [5], [6], [7] because extracting structural features becomes much more difficult, and the mere principal lines do not contribute adequately to high accuracy [6]. Li et al. [4] used four masks to highlight the distribution of line segments in horizontal, vertical and two diagonal lines, and then computed the global and local energies to represent a palmprint image. Wu et al. [5] applied the derivative of Gaussian (DoG) filters to extract palmprint texture and encode to DoGCodes for recognition. Connie et al. [7] combined three wavelet bases and linear projection methods for better performance than that obtained by directly using the original images. Among the approaches for texture analysis, the Gabor function has been regarded as an effective tool due to its optimal localization properties in both spatial and frequency domain [8]. By using a Gabor function of multiple scales and orientations, we can decompose the images into distinctive components. Kong et al. [6] has successfully applied 2D Gabor filter in palmprint recognition. In their method, Gabor features, derived from the convolution of a Gabor filter and palmprint images, were encoded into hamming codes by pixels. But these techniques cannot deal with the variations effectively.
In fact, the variations occurred on palmprint images are inevitable. When capturing a palmprint image and cropping the region of interest (ROI), it is very hard to align the palmprint images in the same precise position, which brings forth rotation and translation. Moreover, the illuminations of captured images vary with the stretching and pressure of palms greatly (see Fig. 1). To address the problem, Kong et al. [6] supposed the image shifts in two directions. They calculated the Hamming distance for each possibility separately, and took the minimum as the final distance. However, it is difficult to confine the shift to a supposed limit, and moreover, the method requires extra cumbersome calculation for each possibility. Therefore, how to extract invariant features against variations of positions and illumination is of great importance for palmprint recognition.
Arivazhagan et al. [8] attempted to obtain rotation invariant features using Gabor functions for texture classification. In their method, texture features were found by calculating the mean and variance of the Gabor filtered image, the rotation invariability was achieved by rotation-normalized, circular shift of feature elements to ensure all the images had the same dominant direction [8]. The approach seems to be effective for the regular textures, containing obvious rotation variations such as barks, bricks, etc. However, the holistic features are not suited for palmprint images, because the texture is non-periodical, and contains minor variations after image alignment.
Therefore, a novel method to extract Gabor-based local invariant features for palmprint recognition is proposed in this paper. Inspired by fractal coding [9], a Gabor filtered image can be partitioned into 4p blocks in which each is divided into 4 sub-blocks, the local relative variance (LRV) is defined as the difference of variance between each lower-layer sub-block and its resided upper-layer block. Eventually, the LRVs of all the 4p+1 sub-blocks, attributed to Gabor filtered images of all scales and orientations; compose the Gabor-based local invariant feature vector to represent a palmprint image. Due to the image localization and variance subtraction, the features are locally invariant to global noises aroused by variations of position and illumination, resulting in better recognition performance. In addition to the higher accuracy, the proposed method is more efficient because the mere one-order statistical calculation of blocks, rather than pixels, is required, resulting in less computation effort.
This paper is organized as follows. Section 2 introduces the local Gabor invariant features for palmprint recognition. The experimental results are discussed in Section 3. Finally, Section 4 highlights the conclusion.
Section snippets
Gabor-based local invariant features for recognition
Generally speaking, a palmprint recognition system mainly contains three stages: preprocessing, feature extraction and feature matching. In the preprocessing stage, the captured palm image is aligned and the center part is cropped as the ROI for recognition. The feature matching stage is to identify the test image as belonging to the class which shows the highest similarity. This paper focuses on the second stage to propose a novel method of extract Gabor-based local invariant features
Palmprint database
We have collected 1460 palmprint images of 292×413 pixels of 72 dpi from 146 palms by a small-scale image scanner, each has 10 samples. The volunteers are required to spread their hands on the surface of the scanner, where we have a reference position for the thumb. The other four fingers can stretch freely, which leads to the rotation and translation of the palm within a small extent. In addition, owing to the diversities of palm pressure, stretching extent and structure, the illuminations of
Conclusion
This paper reports a novel method to extract Gabor-based local invariant features for palmprint recognition. The novelty of this study comes from using the relationship between the local lower-layer sub-blocks and upper-layer blocks based on Gabor features, defined as LRV, to represent palmprint image. As counteraction of the global disturbances and variations, the proposed method achieves obvious improvements in terms of correct recognition rate. At the same time, the proposed method is high
Acknowledgments
The authors are grateful to the anonymous reviewers for their constructive comments and advices. This work is supported partly by the National Natural Science Foundation of China under Grant No. 60472033, No. 60672062, and the National Grand Fundamental Research 973 Program of China under Grant No. 2004CB318005.
Xin Pan received her B.S. and M.S in Xi’an Institute of Technology and Inner Mongolia Agricultural University in 1997 and 2000. She has worked in College of Computer and Information Engineering, Inner Mongolia Agricultural University since then. Now she is pursuing her Ph.D. degree at the institute of information science, Beijing Jiaotong University. Her research interests include image processing, pattern recognition, etc.
References (14)
- et al.
Matching of palmprint
Pattern Recognition Letters
(2002) - et al.
Two novel characteristics in palmprint verification: datum point invariance and Line feature matching
Pattern Recognition
(1999) - et al.
Palmprint feature extraction using 2-D Gabor filters
Pattern Recognition
(2003) - et al.
An automated palmprint recognition system
Image and Vision Computing
(2005) - et al.
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
(2006) - et al.
A hybrid wavelet-based fingerprint matcher
Pattern Recognition
(2007) Palmprint Authentication
(2004)
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2016, Journal of Visual Communication and Image RepresentationCitation Excerpt :Orientation of lines and wrinkles are the most important palmprint features. Palmcode features are proposed on the basis of binarization of Gabor response for a fixed scale and orientation [6]. Competitive code is based on the dominant orientation response of Gabor filter, while robust line orientation code (RLOC) is based on the modified finite Radon transform (MFRAT) in a particular direction [7,8].
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2013, NeurocomputingCitation Excerpt :Pan and Ruan [26] developed GB(2D)2PCA algorithm for palmprint recognition, which used (2D)2PCA to reduce the dimension of Gabor feature space in both row and column directions, resulting in fewer coefficients for feature matching. In 2009, Pan and Ruan [27] also reported a method to extract Gabor-based local invariant features for palmprint recognition by using the relationship between the local lower-layer sub-blocks and upper-layer blocks based on Gabor features. Based on the above discussion, it can be known that the existing improved Gabor based methods mainly focus on the image representations, such as two parts of Gabor feature, i.e., magnitude and phase, or real and imaginary.
Xin Pan received her B.S. and M.S in Xi’an Institute of Technology and Inner Mongolia Agricultural University in 1997 and 2000. She has worked in College of Computer and Information Engineering, Inner Mongolia Agricultural University since then. Now she is pursuing her Ph.D. degree at the institute of information science, Beijing Jiaotong University. Her research interests include image processing, pattern recognition, etc.
Qiuqi Ruan was born in 1944. He received the B.S. and M.S. degrees from Northern Jiaotong University, China in1969 and 1981, respectively. From January 1987 to May 1990, he was a visiting scholar in the University of Pittsburgh, and the University of Cincinnati. Subsequently, he has been a visiting professor in USA for several times. He has published two books and more than 100 papers, and achieved a national patent. Now he is a professor, doctorate supervisor at the institute of information science, Beijing Jiaotong University. He is a senior member of IEEE. His main research interests include digital signal processing, computer vision, pattern recognition, and virtual reality, etc.