A novel approach performed to detect and track face in video sequence by combining 2 different algorithms and is tested across the existing algorithm of same category.

1. Guided By:
Ranganatha S B.E,M.Tech,MISTE
Assistant Professor
Presented By:
4GH12CS019 Karthik G N
Department of Computer Science & Engineering, Government Engineering
College, Hassan
May-2016
Face Detection and Tracking in
Video Sequence
Final phase project evaluation
on....

2. Index
 Introduction
 Problem
 Solution
 Architecture and Design
 Project
 Result Analysis
 Challenges and Constraints
 Conclusion and Future Works
 References

3. Introduction
 Video processing has become a major
requirement in current world.
 This technique is majorly used to detect,
recognize and track various objects.
 Face detection and tracking is the phase where
we detect a person’s face from a video sequence
and track him/her throughout the video.
 It plays vital role in video corrections,
surveillance, military tracking so on.

4. Problem
There are many existing algorithms for face
detection and tracking in video sequences. But
none of them have an accuracy of tracking the
facial region completely.
There is no algorithm till date that tracks all
kinds of facial features in videos under all
possible constraints effectively.

5. Solution
Developing a modified algorithm from
existing algorithms to increase the accuracy. The
increase in tracking accuracy is achieved by
fusing two different algorithms that work based on
similar concepts and similar point of interest. The
new fused Face detection and tracking algorithm
provide more accuracy due to the fact that it
combines two algorithms, it is a simple logic that if
one algorithm fails to track the facial region, other
algorithm keeps track of it and gradually the
accuracy will be improved.

6. Architecture and Design

7. Modules Design

8. Project
 Our project begins with the detection of face in
the 1st frame in the video sequence using Viola-
Jones Algorithm.
 We used the Viola-Jones detector to detect face
in the input video sequence using MATLAB
Toolbox.
 Output of the detector is fed as a input to
masking, masked in such a way that the rest
area apart from the face region in the 1st frame is
masked out.
 We obtain the ROI ( face region in our case ) in
the frame.

9. Continued...
 We apply Gaussian filter on the computed
values.
 After processing all these steps we apply the
Sobel’s edge detector Algorithm on the
modified frame.
 We henceforth obtain all the computer
distinguishable edges in the ROI of the 1st
frame.
 By using these points we find the centroid in the
ROI.
 Tracking starts by calling external function named
next2().
 Tracking uses point tracker to track the points in
facial region of the frames.

10. Continued...
 The new concatenated point’s matrix is fed to the
point tracker of KLT algorithm.
 These points are tracked till last frame of the
video sequence that has been given as input.
 After completion of tracking, the number of
frames that contain bounding box is calculated.
 The resulting value is compared with that of the
value obtained by tracking the same video
sequence in KLT algorithm and results are
tabulated.

11. Result Analysis

12. Snapshots
GUI
Detected Face

13. Contd..
Detected Points
Tracking Face

14. Challenges and Constraints
 The face must be present in the first frame of the input video sequence.
 The video must be recorded only by fixing the camera in one particular
location or fixing the person location and varying the camera.
 Variation in camera position must be negligible, failure in which leads to
increase in complexity while detection and tracking of the faces in video
sequence.
 The input video must be one among many of standard formats used
worldwide, change in which leads to false results.
 As the project fuses various algorithms to increase its efficiency, output
binds with the few of the limitations of each algorithms even after
overcoming most of their drawbacks.
 The resulting system must have only one face detected in the first frame,
in case there are multiple faces detected then the Sobel's algorithm detect
edges but computation of centroid fails leading to failure in tracking of
face(s) in further frames of the video sequence.

15. Conclusion
We have developed a fused Face detection
and tracking system which works based on the
point tracking as that of KLT algorithm. From the
test reports we could clearly observe that fused
FDT algorithm tracks face in few more frames
than KLT algorithm alone would have achieved
and also because we use centroid as one of the
point while tracking, the chances of variation in
bounding box size and shape is very negligible
compared to KLT algorithm alone.

16. Future Works
 Modify Viola-Jones algorithm to remove the
constraint of face being present in first frame
itself.
 Faces can be detected in further frames using a
loop.
 Generating more points using mid-point theorem
from edge points.
 Eliminating the use of Eigen features for tracking,
using point tracker only for the edge points and
other generated points.
 Reducing the execution time by simplifying the
code statements.

17. References
 http://in.mathworks.com/products/image/index.html
 http://in.mathworks.com/help/images/
 http://www.tutorialspoint.com/dip/
 http://in.mathworks.com/academia/students.html?s_tid=ac
main sp_gw_bod
 http://in.mathworks.com/help/matlab/creating guis/about-
the-simple-programmatic-gui-example.html
 Rafel C Gonzalez and Richard EWoods, Digital Image
Processing", 3rd Edition, Pearson Education, 2003.
 Milan Sonka, Vaclav Hlavac and Roger Boyle, Image
Processing, Analysis and Machine Vision", 2nd Edition,
Thomoson Learning, 2001.

18. THANK YOU!!!!