Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Graduation Project - Face Login : A Robust Face Identification System for Sec...Ahmed Gad
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work.
Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system.
There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/.
Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014.
A YouTube video describing the project generally.
https://www.youtube.com/watch?v=OUvaPW70Eko
Find me on:
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Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Graduation Project - Face Login : A Robust Face Identification System for Sec...Ahmed Gad
Face login is my 2015 graduation project started in 2014 and lasted 1.5 years of work.
Generally, it is an identification system using face images. It is a multi-use system but it was mainly created to authorize users to login into their system.
There is an IEEE paper published by the project algorithm used in ICCES 2014 http://ieeexplore.ieee.org/abstract/document/7030929/.
Here is its citation Semary, Noura A., and Ahmed Fawzi Gad. "A proposed framework for robust face identification system." Computer Engineering & Systems (ICCES), 2014 9th International Conference on. IEEE, 2014.
A YouTube video describing the project generally.
https://www.youtube.com/watch?v=OUvaPW70Eko
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Facial recognition is a form of computer vision that uses faces to attempt to identify a person or verify a person’s claimed identity. Regardless of specific
Comprehensive And Integrated Approach To Project Management And Solution Deli...Alan McSweeney
Describes a complete and integrated approach to solution delivery that encompasses project management, project portfolio management, business analysis and solution architecture and design
Effective solution delivery requires an integrated approach to projects across all key disciplines
Project portfolio management
Project management
Business analysis
Solution design
Having silos of expertise that do not communicate or co-operate leads to significant risk
After unnecessary complexity has been reduced from the problem being solved, the scope of the solution to the problem is governed by the complexity of the problem. Complexity is needed to handle and process complexity. Systems acquire or accrete unnecessary complexity over time as originally unforeseen exceptions or changes are incorporated. It may be possible to reduce complexity by collapsing/compressing/combining/consolidating elements and by removing non-value-adding, duplicate, redundant activities. When unnecessary or accreted complexity in the problem being solved has been removed, you are left with necessary complexity that must be incorporated into the solution. Simple problems do not have complex solutions. Complex problems do not have simple solutions. The complexity factor of the proposed solution must match the complexity factor of the problem being resolved. Many system implementation and operational failures arise because of failure to understand and address the core complexity of the problem.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
This report is based on research. This whole research content are taken by books and websites. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we also add face recognition algorithm in report.
The face recognition technique gave in this study utilizes a reconfigurable organization of paramount threshold logic cells and can be utilized in the optional layer of a pixel exhibit. The technology used today for face recognition is neither either new nor particularly ancient. Face recognition is primarily employed for security reasons. Real-time applications have seen rapid growth in the demanding and fascinating field of face recognition. In recent years, face recognition has been the subject of intensive research. This report presents an up-to-date review of key human facial recognition studies. We begin by providing a general introduction of face recognition and its uses. The most recent facial recognition methods are then reviewed in the literature.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
happiness mood of nine persons using subspace methods. This paper mainly focuses on new
robust subspace method which is based on Proposed Euclidean Distance Score Level Fusion
(PEDSLF) using PCA, ICA, SVD methods. All these methods and new robust method is
tested with FGNET database. An automatic recognition of facial expressions is being carried
out. Comparative analysis results surpluses PEDSLF method is more accurate for happiness
emotional facial expression recognition.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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4. Facial recognition systems are built on computer programs
that analyze images of human faces for the purpose of
Identifying them.
The programs take a facial image, measure characteristics such
as the distance between the eyes, the length of the nose, and
the angle of the jaw, and create a unique file called a
"template."
7. Perhaps the most famous early example of a face recognition
system is due to Kohonen , who demonstrated that a simple
neural net could perform face recognition for aligned and
normalized face images.
Kirby and Sirovich (1989) later introduced an algebraic
manipulation which made it easy to directly calculate the
eigenfaces, and showed that fewer than 100 were required
to accurately code carefully aligned and normalized face
images.
Face Recognition using Elastic Graph Matching
8.
9. Laplacianfaces refer to
an appearance-based
approach to human face
representation and
recognition. The approach
uses
Locality Preserving Projection
(LPP) to learn a locality
preserving subspace which
seeks to capture the
intrinsic geometry of the
data and the local
structure.
When the projection is obtained, each face image
in the image space is mapped to the low-
dimensional face subspace, which is characterized
by a set of feature images, they are
called Laplacianfaces.
10. Principle Component Analysis(PCA) is
an eigenvector method designed to
model linear variation in high-
dimensional data.
Locality Preserving Projections (LPP),
the face images are mapped into a
face subspace for analysis.
and Linear Discriminant Analysis
(LDA) which effectively see only the
Euclidean structure of face space,
Two-dimensional linear embedding of face images by Laplacianfaces. As can be
seen, the face images are divided into two parts, the faces with open mouth and
the faces with closed mouth. Moreover, it can be clearly seen that the pose and
expression of human faces change continuously and smoothly,
from top to bottom, from left to right. The bottom images correspond to points
along the right path (linked by solid line illustrating one particular mode of
variability in pose.
14. 2. Image Based Projection Techniques
Laplacian is based upon the processing
of images.
Input Image
Matched Image
Processing
15. KDT Algorithm
The utilization of the KDT algorithm is quite effective in
speeding up the kNN query process.
By adopting the KDT method, the
2D Laplacianfaces is improved to
be not only more efficient for
training, but also as competitively
fast as other methods for query
and classification.
3D Tree
16. Face hallucination
Face hallucination is super-resolution of face images, or
clarifying the details of a face from a low-resolution image. The
technique of sparse coding can be used. Because of the
importance of face images in facial recognition systems and
other applications, face hallucination has become an area of
research.
17. Camera Technology
Cameras can be used to detect the Faces and recognize a
particular person
"Camera technology designed to
spot potential terrorists by their
facial characteristics at airports
failed its first major test at
Boston's Logan Airport"
To Search Someone
18. LIMITATIONS
The human face has 80 nodal points, of which facial
recognition software utilizes 14 to 22.
Less accurate
Only pgm file is used
Does not deal with manifold structure
It doest not deal with biometric characteristics
19. FUTURE SCOPES• A new dimension to facial recognition-3d
• Unobtrusive audio-and-video based person identification systems.
• Neven Vision, www.nevenvision.com a Santa Monica, Calif.-based
developer of mobile machine vision technology.
Neven Vision
3D Face
Expression
Unobtrusive
20. REFERENCES[1] A. U. Batur and M. H. Hayes, “Linear Subspace for Illumination
Robust Face Recognition”, IEEE
Int. Conf. on Computer Vision and Pattern Recognition, Hawaii, Dec. 11-
13, 2001.
[2] P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, “Eigenfaces vs.
Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE
Trans. Pattern Analysis and Machine Intelligence, vol.
19, No. 7, 1997, pp. 711-720.
[3] M. Belkin and P. Niyogi, “Laplacian Eigenmaps and Spectral
Techniques for Embedding and Clustering”, Advances in Neural
Information Processing System 15, Vancouver, British Columbia, Canada,
2001.
[4] M. Belkin and P. Niyogi, “Using Manifold Structure for Partially
Labeled Classification”, Advances
in Neural Information Processing System 15, Vancouver, British
Columbia, Canada, 2002.
[ ]
21. Now We Know
What is Face Recognition?
Its History
What Technology is used?
What are its Features?
Its limitations and Future?