Transcript Automatic Craniofacial Structure Detection on

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
 feature subset
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
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
Proposed Work
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 palmprint texture
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.
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 bestfitting ellipse .
 The counterclockwise rotation of major axis relative to
the normal axis is used to approximate the orientation
θ
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
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.
AUTOMATED EXTRACTION OF HAND-SHAPE AND
PALMPRINT IMAGES
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.
Palmprint Features
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).
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
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)
EXPERIMENTS AND RESULTS
EXPERIMENTS AND RESULTS
EXPERIMENTS AND RESULTS
EXPERIMENTS AND RESULTS
CONCLUSION
 Hand-shape and palmprint image 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.
THE END
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