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Personal Recognition Using Hand Shape and Texture

Personal Recognition Using Hand Shape and Texture. Ajay Kumar , Member, IEEE , and David Zhang , Senior Member, IEEE. Introdution. This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture . Fingerprint iris palmprint voice

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Personal Recognition Using Hand Shape and Texture

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  1. Personal Recognition Using Hand Shape and Texture Ajay Kumar, Member, IEEE, and David Zhang, Senior Member, IEEE

  2. Introdution • This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. • Fingerprint iris palmprint voice • feature subset

  3. Proposed Work • Feature subset selection helps to identify and remove much of the irrelevant and redundant features. • improving the performance of hand-shape recognition by exploring new features • investigating the palmprint recognition in frequency domain using popular discrete cosine coefficients

  4. Proposed Work • Evaluating the performance gain from the feature subset selection and features combination. • Bayes support vector machines(svm) feed-forward neural networks(FFN) K -nearest neighbor (K-NN) decision-tree

  5. Proposed Work

  6. AUTOMATED EXTRACTION OF HAND-SHAPE AND PALMPRINT IMAGES • extraction of these two images 1. a binary image depicting hand-shape 2.a gray-level image containing palmprinttexture

  7. AUTOMATED EXTRACTION OF HAND-SHAPE AND PALMPRINT IMAGES • The magnitude of thresholding limit η is computed by maximizing the object function Jop(η) • where the numbers of pixels in class 1 and 2 are represented by P1(η)and P2(η) ,µ1(η) and µ2(η) are the corresponding sample mean.

  8. AUTOMATED EXTRACTION OF HAND-SHAPE AND PALMPRINT IMAGES • The orientation of each of the binarized image P(x,y) is estimated by the parameters of the best-fitting ellipse is estimated by the parameters of the best-fitting ellipse . • The counterclockwise rotation of major axis relative to the normal axis is used to approximate the orientation θ

  9. AUTOMATED EXTRACTION OF HAND-SHAPE AND PALMPRINT IMAGES ρ11 , ρ22 , and ρ12 are the normalized second-order moments of pixels in the image P(x,y) (cx,cy) denote the location of its centroid

  10. AUTOMATED EXTRACTION OF HAND-SHAPE AND PALMPRINT IMAGES • Remove isolated foreground blobs or holes by morphological preprocessing • The distance transform of every pixel in the hand-shape image is used to estimate the center of palmprint. • The location (u,v)of the pixel with highest magnitude of distance transform is obtained. • All the gray-level pixels from the original hand image, in a fixed-square region, centered at (u,v) and oriented along , are used as the palmprint image.

  11. AUTOMATED EXTRACTION OF HAND-SHAPE AND PALMPRINT IMAGES

  12. Palmprint Features • The discrete cosine transform(DCT) that maps a Q × R spatial image block Ω to its values in frequency domain (fig4) • The feature vector from every palmprint image is formed by computing standard deviation of these significant DCT coefficients in each of these blocks.

  13. Palmprint Features

  14. Hand-Shape Features • We investigated seven such shape properties, i.e., perimeter (f1)，solidity (f2) , extent (f3) eccentricity (f4) , x – y position of centroid relative to shape boundary (f5 - f6), and convex area (f7)to improve the success of prior methods. • In addition, 16 geometrical features from the hand shape, as proposed in prior work were also obtained; four finger length (f8 – f11), eight finger width (f12 – f19), palm width (f20), palm length (f21), hand area (f22), and hand length (f23).

  15. CLASSIFICATION SCHEMES • naive Bayes normal-it traditionally makes the assumption that the feature values are normally distributed estimation- The distribution of features was also estimated using nonparametric kernel density estimation multinomial • K -nearest neighbor (k-NN) - minimum Euclidean distance between the query feature vector and all the prototype training data • support vector machine (SVM

  16. CLASSIFICATION SCHEMES • The feed-forward neural network (FFN) - a linear activation function for the last layer the sigmoid activation function was employed for other layers • The C4.5 decision tree • logistic model tree (LMT)

  17. EXPERIMENTS AND RESULTS

  18. EXPERIMENTS AND RESULTS

  19. EXPERIMENTS AND RESULTS

  20. EXPERIMENTS AND RESULTS

  21. CONCLUSION • Hand-shape and palmprintimage segmentation, and the combination of features from these two images, has shown to be useful in achieving higher performance. • Our experimental results in Section V suggested the usefulness of shape properties (e.g., perimeter, extent, convex area) which can be effectively used to enhance the performance in hand-shape recognition. • Although more work remains to be done, our results to date indicate that the combination of hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.  

  22. THE END thanks for your listening

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