Handwritten Text Recognition for manuscripts and early printed texts
Face recognition
1.
2. PRESENTATION OUTCOMES
• What is face Recognition?
• How facial recognition works ?
• Face detection and recognition.
• Different approaches of face Recognition.
– Feature extraction methods
– Holistic methods
– Hybrid methods
• Problems
• Applications Available in Market
3. ABSTRACT
• Images play an important role in todays information because A
single image represents a thousand words.
• Google's image search, where we can easily search for images
using keywords.
Getting the computer to understand the semantics inside of images
isn't easy. The reason for this is simply because the computer isn't
able to understand the context.
4. Find the human face in the display as fast as you can. Ready?
6. • Getting the computer to understand
the semantics inside of images isn't
easy. The reason for this is simply
because the computer isn't able to
understand the context.
10. FACE DETECTION + RECOGNITION
• Detection accuracy affects the recognition
stage
• Key issues:
– Correct location of key facial features
(e.g. the eye corners)
– False detection
– Missed detection
11. DIFFERENT APPROACHE
• Describe the different methods of face
recognition.
– Feature extraction methods
– Holistic methods
– Hybrid methods
12. 1. FEATURE EXTRACTION METHODS
• Feature extraction is the task where we locate
facial features,
– Eg: the eyes, the nose, and the chins etc.
This task may be performed after the face detection task Or
recognition time.
• big challenge for feature extraction methods is feature
“restoration“.
– Facial features are invisible according to the large variation.
13. FEATURE EXTRACTION METHODS
• This method is widely used to create
individual vectors for each person in a
system, the vectors are matched when an
input image is being recognized.
15. 2. HOLISTIC METHODS
• Holistic methods uses the whole face region as
the input to a recognition system.
• focuses a holistic method using eigenfaces to
recognize still faces.
16. FACE RECOGNITION USING EIGENFACES
1. The first stage is to insert a set of images into a database, these images are
called the training set, this is because they will be used when we compare
images and when we create the eigenfaces.
2. The second stage is to create the eigenfaces. Eigenfaces can now be extracted
from the image data
3. When the eigenfaces have been created, each image will be represented as a
vector of weights.
4. The system is now ready to accept incoming queries.
17. FACE RECOGNITION USING EIGENFACES
5. The weight of the incoming unknown image is found and then
compared to the weights of those already in the system. If the input
image's weight is over a given threshold it is considered to be
unknown. The identification of the input image is done by finding the
image in the database whose weights are the closest to the weights of
the input image. The image in the database with the closest weight
will be returned as a hit to the user of the system.
18. 3. HYBRID METHODS
• Hybrid face recognition systems uses a combination of both
holistic and feature extraction methods.
• Hybrid method of face recognition by using 3D model. The
model makes it possible to change the pose and the
illumination on the face.
19. 3D MORPHABLE MODEL
• Took face recognition to a new level. By being
able to use a morphable 3D model to create
synthetic images has proven to give good
results. It is a very applicable approach that
solves many of the problems.
system achieved a recognition rate
of 90%.
20. Problems of Face Recognition
• when comparing a database image with an input image.
The main concern is of course that all images of the same
face are heterogeneous.
• When image databases are created they contain good
scenario images.
21. • concerning deferent facial
expressions as well. The system
must be able to know that two
images of the same person with
deferent facial expressions actually
is the same person. Makeup, posing
positions, illumination conditions,
and comparing images of the same
person with and without glasses.
22. • Fastest and safest method of tracking
employee time and attendance.
• Easy to install and use.
• Cost saving and convenient way of time
tracking.
• Provide easy and efficient way of recording
attendance.
• Easily manage employee time and attendance
profiles.
• Get rid of buddy punching.
• Also manage employee payroll record.
• On-demand time attendance record for
reference.
• Easily customizable as per your requirement.
Applications Available in Market
Face Recognition based Time Attendance System
23. Applications Available in Market
Access Control System
• Convenient and secure method of controlling
door entry
• Authentication by Facial Biometrics to gain
entry
• Higher security than conventional systems
• No keys or cards to carry
• No need to issue keys or cards for every user
• Accurate recording of arrivals and departures
• Real time monitoring of door access
• Intelligent access control by group or time
schedule
24. Applications Available in Market
Facial Recognition PC Security
Logon provides a simple but effective
option. The integration of Logon and PC
camera provides access only when a live-fed
face image of authorized user is
detected, thus effectively preventing
unauthorized access. Logon is a non-invasive
technology that does not require physical
contact.
25. Applications Available in Market
Face Biometric Login Through Web
• Embeddable in any web page
• Global Face Authentication capability
• Free version available
• View of authenticated clients
• Messaging to Clients possible
• Remote Backup/Restore
Google's Picasa, Facebook
Facial Recognition Software in
Online Gaming and Crime Prevention
