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Register a new userAn Enhanced Method for Recognizing Face on the Basis of Facial Expressions and Skin Detection
طريقة مُدعمة للتعرف على الوجه مبنية على التعابير الوجهية و اكتشاف الجلد
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Aِl-Baath University ورقة بحثية
Publication date
2014
fields
Mathematics
and research's language is
العربية
Created by
Shamra Editor
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يعرض هذا البحث طريقة جديدة للتعرف على الوجه في حالات انفعالية مختلفة. تعتمد هذه الطريقة على خوارزميتنا المقترحة SD.R&C لاكتشاف الجلد البشري و تحديد الوجه, و على تصنيف نوع التعبير.
This paper presents a new method to recognize human face in different emotional situations. This method is based on proposed algorithm SD.R&C to discover skin and expression classification.
References used
AIFANTI N.; PAPACHRISTOU C.; DELOPOULOS A., 2010-The MUG Facial Expression Database. Proc. 11th Int. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Desenzano, Italy
ALIAA A., YOUSSIF A., WESAM A., 2011- Automatic Facial Expression Recognition System Based on Geometric and Appearance Feature, Computer and Information Science, Published by Canadian Center of Science and Education, Vol. 4, No. 2, Pages 115-124
Calvo A., Ruiz F., Rurainsky J., Eisert P., 2008- 2D-3D Mixed Face Recognition Schemes, I-Tech, Vienna, Austria, pp. 125-148
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The aim of the work is to improve the performance of the WLD
descriptor using Gabor filters in a preprocessing stage. The
performance of the improved descriptor will be compared with the
performance of the LBP descriptor(a widely used descriptor i
n FER
researches). This performance will be achieved using the extremely
used expert system SVM besides the expert systems CSD and MLP.
التعرف على تعابير الوجه
النماذج الثنائية المحلية
واصف ويبر المحلي
نماذج غير المحلية الثنائية
واصف ويبر-غيبر المحلي
مرشح غيبر
الشبكة العصبونية متعددة الطبقات
أداة الأشعة الداعمة
مسافة تشي التربيعية
FER-Facial Expression Recognition
LBP-Local Binary Pattern
WLD-Weber Local Descriptor
LGBP-Local Gabor Binary Pattern
WGLD-Weber Gabor Local Descriptor. Gabor filter
MLP-Multi Layer Perceptron
SVM- Support Vector Machine
CSD- Chi squared distance
المزيد..
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 amount of digital images that are produced in hospitals is increasing rapidly. Effective
medical images can play an important role in aiding in diagnosis and treatment, they can
also be useful in the education domain for healthcare students by
explaining with these
images will help them in their studies, new trends for image retrieval using automatic
image classification has been investigated for the past few years. Medical image
Classification can play an important role in diagnostic and teaching purposes in medicine.
For these purposes different imaging modalities are used. There are many classifications
created for medical images using both grey-scale and color medical images. In this paper,
different algorithms in every step involved in medical image processing have been studied.
One way is the algorithms of preprocessing step such as Median filter [1], Histogram
equalization (HE) [2], Dynamic histogram equalization (DHE), and Contrast Limited
Adaptive Histogram Equalization (CLAHE). Second way is the Feature Selection and
Extraction step [3,4], such as Gray Level Co-occurrence Matrix(GLCM). Third way is the
classification techniques step, which is divided into three ways in this paper, first one is
texture classification techniques, second one is neural network classification techniques,
and the third one is K-Nearest Neighbor classification techniques.
In this paper, we have use MRI brain image to determine the area of tumor in brain. The
steps started by preprocessing operation to the image before inputting it to algorithm. The
image was converted to gray scale, later on remove film artifact using special algorithm,
and then remove the Skull portions from the image without effect on white and gray matter
of the brain using another algorithm, After that the image enhanced using optimized
median filter algorithm and remove Impurities that produced from first and second steps.
On Wikipedia, an online crowdsourced encyclopedia, volunteers enforce the encyclopedia's editorial policies. Wikipedia's policy on maintaining a neutral point of view has inspired recent research on bias detection, including weasel words'' and hedges
''. Yet to date, little work has been done on identifying puffery,'' phrases that are overly positive without a verifiable source. We demonstrate that collecting training data for this task requires some care, and construct a dataset by combining Wikipedia editorial annotations and information retrieval techniques. We compare several approaches to predicting puffery, and achieve 0.963 f1 score by incorporating citation features into a RoBERTa model. Finally, we demonstrate how to integrate our model with Wikipedia's public infrastructure to give back to the Wikipedia editor community.
A group of 200 subjects was divided into five groups consisting of
dentist, students of the last year predoctoral dental, students of
pediatric dentistry, students of dental assistants, and pediatric
dentists. The groups were surveyed in order to
elicit their
responses to a series of ten simple line-drawn faces. Each person
was asked in a questionnaire to medicate on a five-point scale the
degree to which an adjective was appropriate or inappropriate for
each simple line-drawn face. Twenty adjectives were repeated for
the ten different faces. Means and standard deviations were
calculated for all responses. Results indicate that each face yielded
a unique set of appropriate adjectives as perceived by all subjects.
Beyond this basic agreement, however, group differences were
found in the selection of other appropriate adjectives. Based upon
a random subset of responses, discriminant analysis correctly
assigned group membership for 96% of the subjects. It was also
found that eight of the ten faces discriminated among the groups
with correct reclassification ranging from 88% to 96%.
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