The research presents a design for an automated checking system for students. The
system takes a picture of the student, then it extracts his/her basic facial features. The
network was trained using the reverse spreading algorithm. If a training da
tabase is
generated for each student consisting of 15 training samples contained of the necessary
facial expressions to identify the student for one time at the beginning of the semester, then
the neural network will be trained on students database to obtain a trained neural network
able to identify the students of each category depending on their physical appearance. That
will result in knowing who attends and who does not attend the session. The system
designed for this purpose was supplied with the trained network. The system provides the
possibility of automated checking for students according to the content of the study giving
the alarm in case of the existence of the picture of a student who does not belong to the
same group.
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.
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.
This Research suggests a new mechanism that aims to increase the effectiveness of
surveillance systems by extracting the moving objects coming from surveillance camera in
order to identify them and propose a new mechanism for indexing and storing i
n database
and classified them according to the basic characteristics and strong indicators and retrieval
when needed in less possible time.
The basic idea lies in the combination of the basic characteristics of the goal (color,
edges and texture) which ensures the best performance in extracting the basic target
features and depend on it as indexes, then nonlinear transfers has been done on the edges
of the target in order to get a picture bearing the minutest details, then conducted adverse
transfers on the edges of the target during the process retrieved from the database. Finally,
we propose a new mechanism for indexing all images tabase to Retrieval them in best
accuracy and less time, and a program had been achieved to realize this idea.
يهدف البحث إلى تقديم دراسة مرجعيّة مفصلة عن استخدام الشبكات العصبونية الإلتفافية (CNNs) في استخراج الميزات (Features) من الصور.
وسيتطرق البحث إلى التعريف بمعنى الميزات (Features) الخاصة بالصور وأهميتها في تطبيقات معالجة الصورة.
وسيتم أيضاً التعريف
بالشبكات العصبونية الإلتفافية (CNNs) وبنيتها و طريقة عملها وأنواع المقاربات والمنهجيات المستخدمة في تدريبها لاستخراج الميزات (Features) من الصور.