OBJECTIVE
• Detection of multiple faces from a still image. • Creating a mask in order to fit on the face. • Detection and features tracking of single face from video. • Detection and features tracking of multiple faces from video.
MOTIVATION
• The wide range of commercial and law enforcement applications. • The availability of feasible technologies for research and projects. • Current systems are still far away from the capability to cope up with real time applications.
DIFFICULTIES
• • • • Face Variation Face position Wearing glasses Low resolution images
INTRODUCTION
• Face detection It is the task of localizing faces in an input image, is a fundamental part of any face processing system. The extracted faces can then be used for initializing face tracking or automatic face recognition.
• Face feature tracking It is the process of locating a moving face or several of them over a period of time and then locating facial features like eyes, nose and mouth.
IMPLEMENTATION
CONTD…
RESULTS
Actual image Processed image
RESULTS CONTD…
Actual video frame Processed video frame
RESULTS CONTD…
Actual video frame Processed video frame
APPLICATIONS
• • • • • Access control Identification systems Biometrics Video surveillance Human computer interface
LIMITATIONS
• Due to bad lighting condition, tracking is sometimes not possible. • Due to partially covered faces, tracking is not possible. • Fully rotated faces are not possible to track.
CONCLUSION
• We proposed a modified method for automatic fitting of a deformable face mask to a previous unseen face. • By making mask we are able to fit this mask to different input images and video. • The face model is built with the high and low image resolution. • Successful implementation on multiple faces.
FUTURE SCOPE
• Work can be done to link the project with database for security purposes. • Further processing can be done for rotated faces. • Further processing can be done for blurred images and videos.
REFERENCES
• P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features”, vol. 2, pp. 240-249, 2003. • J.G. Wang and E . Sung, “Frontal view detection and facial features extraction using color and morphological operations”, Pattern recognition letters, vol. 20, pp. 1053-1068, oct . 1999. • W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, 2003. • Digital Image Processing Using MATLAB by Gonzalez, Woods & Eddins, Prentice, pp.25-29.