Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEvaluation of Features Selection in Enhancing the Performance of Palm Print Recognition
تقويم فعالية اختيار السمات الأفضل في تحسين التعرف على الأشخاص باستخدام صورة راحة اليد
1160
0 98
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تقدم الدراسة طريقة جديدة لاستخلاص سمات صورة راحة اليد و تحديد السمات الأكثر فعالية منها، إضافة إلى دراسة تأثير عملية الاختيار على تسريع عمل مراحل النظام و رفع أدائه.
This paper introduces a new approach to extract palm print features and select the best ones. The paper also studies the effectiveness of the selection process on speed and performance of system.
References used
(Doublet J., Lepetit O. and Revenu M., 2006 "Contact-less Hand Recognition using shape and texture features", 8th International Conference on Signal Processing, Vol (3
Funada. J., Ohta N., Mizoguchi M., Temma T., Nakanishi K., Murai A., Sugiuchi T., Wakabayashi T., and Yamada Y 1998 “Feature extraction method for palmprint considering elimination of creases,” Proc.14th International Conference of Pattern Recognition, vol(2), pp. 1849 -1854
Han C. -C., Cheng H.-L., Lin C.-L. and Fan K.-C., 2003 "Personal authentication using palm print features," Pattern Recognition Journal, vol (36), pp. 371-381
rate research
Read More
Personal identification based on handprint has been gaining more attention with the
increasing needs of high level of security. In this study a novel approach for human
recognition based on handprint is proposed. Wavelet transform was used to extra
ct features
presented in the palm image based on wavelet zero-crossing method. Firstly the wavelet
transform of the whole palm image at the fourth level was worked out, which results in
four matrices; three of them are detail matrices (i.e., horizontal, vertical and diagonal) as
well as one approximation matrix. Throughout this study, only the detail matrices were
used because the required information (i.e., hand lines and curves) is included in those
matrices. Sixteen features were extracted from each detail matrix, and then arranged in one
vector. Consequently, for each palm sample a feature vector consisting of 48 input features
of the used neural network was obtained. For this purpose, a database consisting of 400
palm images belonging to 40 people at the rate of 10 images per person was built. Practical
tests outcome showed that the designed system successfully indentified 91.36% of the
tested images.
The study suggests designing a weighting model for iris features and selection of the
best ones to show the effect of weighting and selection process on system performance.
The search introduces a new weighting and fusion algorithm depends on the i
nter and intra
class differences and the fuzzy logic. The output of the algorithm is the feature’s weight of
the selected features. The designed system consists of four stages which are iris
segmentation, feature extraction, feature weighting_selection_fusion model
implementation and recognition. System suggests using region descriptors for defining the
center and radius of iris region, then the iris is cropped and transformed into the polar
coordinates via rotation and selection of radius-size pixels of fixed window from center to
circumference. Feature extraction stage is done by wavelet vertical details and the
statistical metrics of 1st and 2nd derivative of normalized iris image. At weighting and
fusion step the best features are selected and fused for classification stage which is done by
distance classifier. The algorithm is applied on CASIA database which consists of iris
images related to 250 persons. It achieved 100% segmentation precision and 98.7%
recognition rate. The results show that segmentation algorithm is robust against
illumination and rotation variations and occlusion by eye lash and lid, and the
weighting_selection_fusion algorithm enhances the system performance.
The speech recognition is one of the most modern technologies, which entered force
in various fields of life, whether medical or security or industrial techniques. Accordingly,
many related systems were developed, which differ from each otherin fea
ture extraction
methods and classification methods.
In this research,three systems have been created for speech recognition.They differ
from each other in the used methods during the stage of features extraction.While the first
system used MFCC algorithm, the second system used LPCC algorithm, and the third
system used PLP algorithm.All these three systems used HMM as classifier.
At the first, the performance of the speechrecognitionprocesswas studied and
evaluatedfor all the proposedsystems separately. After that, the combination algorithm was
applied separately on eachpair of the studied system algorithmsin order to study the effect
of using the combination algorithm onthe improvement of the speech recognition process.
Twokinds of errors(simultaneous errors and dependent errors) were usedto evaluate
the complementaryof each pair of the studied systems, and to study the effectiveness of the
combination on improving the performance of speech recognition process. It can be seen
from the results of the comparison that the best improvement ratio of speech recognition
has been obtained in the case of collection MFCC and PLP algorithms with recognition
ratio of 93.4%.
Designing Computerized Systems which posses reading and hearing
faculties is an active research area for more than four decades. Many
methods and algorithms have been suggested by researches for this
purpose as part of pattern recognition research
. Recently, more
research work has been devoted to the holist approach the
recognition system recognizes a complete word as one object without
going through the long and erroneous character segmentation
process. In this paper, a convolutional neural network has been
designed to recognize the popular Arabic names holistically. SUSt
ARG names data set has been used to test the network performance
(collected and compiled by pattern recognition research in Sudan
University of Science and Technology-SUSt). Selecting an appropriate
deep learning toolbox, after five stages of training, the network was
able to recognize all the names and 100%
This paper presents a new technique to extract the features of a
common case of images of the iris called off-angle iris which
taken for persons identification system. The main problem when
using biological iris measurements to identify the persons is the
difficulty of identifying and extracting features of the iris. This
problem increasing when dealing with off-angle iris and it leading
to decrease system accuracy and increase system rate error.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
