28-09-2012, 12:29 PM
A Palmprint Feature Extraction and Pattern Classification Based on Hybrid PSO-K-Means Clustering
A Palmprint Feature Extraction.pdf (Size: 504.9 KB / Downloads: 39)
Abstract
The paper presents a hybrid particle swarm optimization (PSO)
technique for optimally clustering N palmprint data points into
K cluster. The cluster center are automatically detected by PSO
technique from a set of features obtained by applying geometrical
methods to the palmprint data. The image captured by a peg-free
scanner, a rectangular region of interest (ROI) containing only the
heart line is extracted. Intensity of the ROI image is standardized
and image is smoothened. After that soble gradient with a threshold
is applied to extract the heart line from the ROI. The cluster centers
are useful in identifying the class of the palmprint, which occurs
in a variety of practical system in engineering and science.
Introduction
Many biometrics techniques have been introduced which includes
fingerprint, iris, face, speech recognition. Recently Palmprints
have come forward as a very reliable biometrics. palm is an inner
surface of the hand between the wrist and the fingers [13]. Palm
has several features to be extracted like principal lines, wrinkles,
ridges, singular points, texture and minutiae. Low-resolution
images are sufficient for the principal lines extraction since they
are thick in nature. There are usually three principal lines made by
flexing the hand and wrist in the palm, which are named as heart
line, head line, and life line respectively. A palmprint image with
principal lines and wrinkles represented is shown in Fig. 1.
Preprocessing and segmentation
1. Image is converted to binary Fig. 2,
2. Boundary tracing 8-connected pixels algorithm is applied on
the binary image to find the boundary of palmprint image Fig. 3.
The starting point is the bottom left point “P” as shown in Fig. 4
and the tracing direction is counter clockwise. The end point is
also “P”. And these boundary pixels are collected in Boundary
pixel vector (BPV).
K-Means Clustering
One of the most important components of a clustering algorithm is
the measure of similarity used to determine how close two patterns
are to one another. K-means clustering groups data vectors into
a predefined number of clusters, based on Euclidean distance
as similarity measure. Data vectors within a cluster have small
Euclidean distances from one another, and are associated with one
centroid vector, which represents the "midpoint" of that cluster.
The centroid vector is the mean of the data vectors that belong to
the corresponding cluster.
Hybrid PSO and K-means Clustering Algorithm
The K-means algorithm tends to converge faster (after less
function evaluation) than the PSO, but usually with a less accurate
clustering [6]. This section shows that the performance of the PSO
clustering can further be improved by sending the initial swarm
with the result of the K-means algorithm. The hybrid algorithm
first executes K-means algorithm once. In this case the K-means
clustering is terminated when (i) the maximum number of iteration
is exceeded, or when (ii) the average change in centroid vector is
less than that 0.0001 (a user specified parameter). The result of the
K-means algorithm is then used as one of the particle, while the
rest of the swarm is initialized randomly. The gbest PSO algorithm
as presented above is then executed.