Palm Vein Pattern Recognition using Matlab

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Description

The project presents robust palm vein recognition using hybrid texture descriptors such as discriminative robust local ternary pattern and Weber?s local descriptor for improving the recognition accuracy. A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. There was a negative effect on recognition performance on fingerprint and palmprint biometrics due to the some conditions such as oil on the fingers, moisture, and dirt. Therefore, vein patterns stand out from the host of intrinsic biometric traits for development of a recognition system that can meet all these expectations. Vein patterns are the network structure of blood vessels underneath the human skin that are almost invisible to the naked eye under natural lighting conditions and can be acquired in vivo only when employing infrared illumination, which effectively protects against possible external damage, spoof attacks, and impersonation. The texture of the blood vessels of different individuals has been proven to be distinctive even among identical twins. Initially the palm vein images are preprocessed to select the region of interest for vein pattern extraction. Here, local thresholding is used to extract the vein pattern for its texture analysis. Two textures descriptors called Weber?s local descriptors and DRLTP are proposed to extract the features about texture for recognizing with original templates. DRLTP is used to provide the shape and contrast invariant features of an object. WLD provides details about illumination changes between the pixels. Euclidean distance will be used to match the features of test and original templates for making decision on a person biometrics. Finally, the performance of proposed algorithm will be measured with recognition accuracy and it proves that it provides better matching rate than prior approaches.


EXISTING METHOD

  • Fingerprint/vein based person authentication
  • Gabor filter and Discrete wavelet transform
  • PCA and Local binary pattern

DRAWBACKS

  • Less efficiency and not flexible in authentication scheme
  • Poor discriminatory power
  • Inefficient texture features due to shift variance
  • Less accuracy for various lighting condition ofimages due to the delivery of insufficient descriptors.

PROPOSED METHOD

Robust palm vein pattern recognition system based on,

  • Vein Pattern Analysis using Discriminative robust local ternary pattern and Weber?s local descriptor
  • NN

BLOCK DIAGRAM

Palm Vein Pattern Recognition using Matlab 1

DRLTP


Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) methods for feature extraction. The system proposes new approach in extension with a local ternary pattern called DRLTP and DRLBP. By using these methods, the category recognition system will be developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. DRLTP & DRLBP is used for different object texture, edge contour and shape feature extraction process. It is robust to illumination and contrast variations as it only considers the signs of the pixel differences. The proposed features retain the contrast information of image patterns. They contain both edge and texture information which is desirable for object recognition. The DRLBP & DRLTP discriminates an object like the object surface texture and the object shape formed by its boundary. The boundary often shows much higher contrast between the object and the background than the surface texture. Differentiating the boundary from the surface texture brings additional discriminatory information because the boundary contains the shape information. These features are useful to distinguish the maximum number of samples accurately and it is matched with already stored image samples for similar category classification.

Weber?s Local Descriptor

The proposed method has the following two characteristics:

(1) Local features around landmarks can well describe the similarity between two images under pose variations and simultaneously reduce redundant information and

(2) Fusion features constructed by randomly selecting local features from predefined regions further alleviate the influence of pose variations. Extensive experimental results on public face datasets have shown that the proposed method greatly outperforms the previous state-of-the-art algorithms.

Euclidean Distance

The Euclidean distance between two points in Euclidean space is the length of a line segment between the two points.?It can be calculated from the Cartesian coordinates of the points using the Pythagorean Theorem, therefore occasionally being called the Pythagorean distance. The distance between two objects that are not points is usually defined to be the smallest distance among pairs of points from the two objects. Formulas are known for computing distances between different types of objects, such as the distance from a point to a line. In advanced mathematics, the concept of distance has been generalized to abstract metric spaces, and other distances than Euclidean have been studied. In some applications in statistics and optimization, the square of the Euclidean distance is used instead of the distance itself.

IMAGE SEGMENTATION

Image segmentation is a process of partitioning an image into nonintersecting regions such that each region is homogeneous and the union of two adjacent regions is not homogeneous. Thresholding-based methods can be classified according to global or local thresholding and also as either bi-level thresholding or multi thresholding.

For the aforementioned facts, we decided to consider the nonparametric and unsupervised Otsu?s thresholding method.


ADVANTAGES

  • Low complexity and better flexibility in vein pattern extraction
  • Descriptors provides local contrast and luminance invariant features
  • It provides better recognition accuracy

SOFTWARE REQUIREMENT

  • MATLAB 2014a or above versions

REFERENCES

[1] Y. Zhou and A. Kumar, ?Human identification using palm-vein images,?IEEE Trans. Inf. Forensics Security, vol. 6, no. 4, pp. 1259?1274,Dec. 2011.

[2] L. Mirmohamadsadeghi and A. Drygajlo, ?Palm vein recognition withlocal binary patterns and local derivative patterns,? in Proc. Int. JointConf. Biometrics, Oct. 2011, pp. 1?6

[3] M. Fischer, M. Rybnicek, and S. Tjoa, ?A novel palm vein recognitionapproach based on enhanced local Gabor binary patterns histogramsequence,? in Proc. 19th Int. Conf. Syst., Signals, Image Process.,Apr. 2012, pp. 429?432.

[4] J.-C. Lee, ?A novel biometric system based on palm vein image,? PatternRecognit. Lett., vol. 33, no. 12, pp. 1520?1528, Sep. 2012.

[5] Y. Zhou and A. Kumar, ?Contactless palm vein identification usingmultiple representations,? in Proc. 4th IEEE Int. Conf. Biometrics,Theory Appl. Syst., Sep. 2010, pp. 1?6.

[6] E. C. Lee, H. C. Lee, and K. R. Park, ?Finger vein recognitionusing minutia-based alignment and local binary pattern-based featureextraction,? Int. J. Imag. Syst. Technol., vol. 19, no. 3, pp. 179?186,2009.

[7] H. C. Lee, B. J. Kang, E. C. Lee, and K. R. Park, ?Finger veinrecognition using weighted local binary pattern code based on a supportvector machine,? J. Zhejiang Univ. Sci. C, vol. 11, no. 7, pp. 514?524,2010.

[8] W. Kang, Y. Liu, Q. Wu, and X. Yue, ?Contact-free palm-vein recognitionbased on local invariant features,? PLoS One, vol. 9, no. 5,p. e97548, 2014

[9] P.-O. Ladoux, C. Rosenberger, and B. Dorizzi, ?Palm vein verificationsystem based on SIFT matching,? in Proc. 3rd Int. Conf. Adv. Biometrics,2009, pp. 1290?1298.

[10] M. Pan and W. Kang, ?Palm vein recognition based on three local invariantfeature extraction algorithms,? in Proc. 6th Chin. Conf. BiometricRecognit., 2011, pp. 116?124.


 

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