Face Recognition Across Non-Uniform Motion Blur, Illumination & Pose IEEE (Only an overview, for seminars and presentations)

1. Face Recognition Across
Non-Uniform Motion Blur,
Illumination & Pose

2. Contents
 Biometric, its types
 Face recognition
 Advantages
 Across Non-Uniform Motion Blur, Illumination, and Pose
 Template creation
 Nodal points
 Process
 Modules
 Conclusion
 Reference

3. What is meant by Biometrics?
 A biometric is a unique, measurable characteristic of a
human being that can be used to automatically recognize
an individual or verify an individual’s identity.
 Biometrics can measure both
physiological and
behavioral characteristics.

4. PHYSIOLOGICAL
a. Finger-scan
b. Facial Recognition
c. Iris-scan
d. Retina-scan
e. Hand-scan
BEHAVIORAL
a. Voice-scan
b. Signature-scan
c. Keystroke-scan

5. CONTENT

6. Why Face Recognition…
 Everyday actions are increasingly being handled electronically, instead
of pencil and paper or face to face.
 This growth in electronic transactions results in great demand for fast
and accurate user identification and authentication.
 Access codes for buildings, banks accounts and computer systems often
use PIN's for identification and security clearances.
 Using the proper PIN gains access, but the user of the PIN is not
verified. When credit and ATM cards are lost or stolen, an unauthorized
user can often come up with the correct personal codes.
 Face recognition technology may solve this problem since a face is
undeniably connected to its owner expect in the case of identical twins.

7. Advantages of using Face Recognition
 It requires no physical interaction on behalf of the user.
 It is accurate and allows for high enrolment and
verification rates.
 It can use your existing hardware infrastructure, existing
cameras and image capture devices, with no problems.

8. Face Recognition… How?
In Facial recognition there are two types of comparisons:-
 VERIFICATION- The system compares the given individual
with who they say they are and gives a yes or no decision.
 IDENTIFICATION- The system compares the given
individual to all the Other individuals in the database and
gives a ranked list of matches.

9.  All identification or authentication technologies operate using
the following four stages:
 Capture: A physical or behavioral sample is captured by the
system during Enrollment and also in identification or
verification process.
 Extraction: unique data is extracted from the sample and a
template is created.
 Comparison: the template is then compared with a new
sample.
 Match/non-match: the system decides if the features
extracted from the new samples are a match or a non match.

10. Across Non-Uniform Motion
Blur, Illumination, and Pose
 Enrolment templates are normally created from a multiplicity
of processed facial images.
 These templates can vary in size from less than 100 bytes,
generated through certain vendors and to over 3K for
templates.
 The 3K template is by far the largest among technologies
considered physiological biometrics.
 Larger templates are normally associated with behavioural
biometrics.

11. Template creation

12. How Facial Recognition System Works
 Facial recognition software is based on the ability to first
recognize faces, which is a technological feat in itself. If
you look at the mirror, you can see that your face has
certain distinguishable landmarks. These are the peaks
and valleys that make up the different facial features.
 There are about 80 nodal points on a human face.

13. Nodal points used
Here are few nodal points that are measured by the
software.
1. distance between the eyes
2. width of the nose
3. depth of the eye socket
4. cheekbones
5. jaw line
6. chin

14.  Detection- when the system is attached to a video surveilance
system, the recognition software searches the field of view of
a video camera for faces. If there is a face in the view, it is
detected within a fraction of a second. A multi-scale algorithm
is used to search for faces in low resolution. The system
switches to a high-resolution search only after a head-like
shape is detected.
 Alignment- Once a face is detected, the system determines the
head's position, size and pose. A face needs to be turned at
least 35 degrees toward the camera for the system to register
it.

15. Face recognition systems that work with focused images have difficulty when
presented with blurred data. Approaches to face recognition from blurred
images can be broadly classified into four categories.
 (i) Deblurring-based in which the probe image is first deblurred and then used for
recognition. However, deblurring artifacts are a major source of error especially for
moderate to heavy blurs.
 (ii) Joint deblurring and recognition, the flip-side of which is computational
complexity.
 (iii) Deriving blur-invariant features for recognition. But these are effective only for
mild blurs.
 (iv) The direct recognition approach in which reblurred versions from the gallery are
compared with the blurred probe image.
It is important to note that all of
 the above approaches assume a simplistic space-invariant blur model. For
handling illumination, there have mainly been two directions of pursuit
based on
 (i) the 9D subspace model for face and
 (ii) extracting and matching illumination insensitive facial features

16. (a) Focused image,
(b) synthetically blurred image obtained by
applying random in-plane translations and
rotations on the focused image,
(c) point spread functions (PSF) at various
locations in the image showing the
presence of non-uniform blur which cannot
be explained by the convolution
model
and (d, e, f) real blurred images

17. Process
 Normalization-The image of the head is scaled and rotated so
that it can be registered and mapped into an appropriate size
and pose. Normalization is performed regardless of the head's
location and distance from the camera. Light does not impact
the normalization process.
 Representation-The system translates the facial data into a
unique code. This coding process allows for easier comparison
of the newly acquired facial data to stored facial data.
 Matching- The newly acquired facial data is compared to the
stored data and (ideally) linked to at least one stored facial
representation.

18. Modules
 Motion blur model for faces
 Face recognition across blur
 Multiscale implementation
 Recognition across blur, illumination and pose

19. Modular description
Motion blur model for faces
 The apparent motion of scene points in the image will vary at
different locations.
 The single blur kernel can’t explain this.
 In the proposed method a space variant motion blur model is
presented and the explanation for geometric degradations of
faces resulting from camera motion is illustrated.
 An optimization algorithm to recover the camera motion is
proposed.

20. Multiscale implementation
 The difference in the displacement of a point light source due
to two different transformations from the discrete set T is at
least one pixel.
 Doubling the sampling resolution increases the total number of
poses.
Face recognition across blur
 We have M face classes with one focussed face fm for each class
m, m=1,2,… ,M. A convex combination of these are then
produced.

21. Recognition across blur, illumination and pose
 Finally pose variation is allowed, in addition to blur and
illumination.
 Four near frontal poses are selected, angles within ~15 degree
and an algorithm called MOBIL is applied in small variations in
pose
 A correct pose is then obtained.

22. Conclusion
 A methodology to perform face recognition under the combined
effects of non-uniform blur, illumination, and pose is proposed.
The set of all images obtained from a given image by non-
uniform blurring and changes in illumination forms a bi-convex
set, this result is used to develop non-uniform motion blur and
illumination-robust algorithm called MOBIL.
 The limitation of his approach is that significant occlusions and
large changes in facial expressions cannot be handled.

23. References
• W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A
literature survey,”
• http://www.nec.com/en/global/solutions/biometrics/technologies/face_rec
ognition.html

24. Thank you