Description

ABSTRACT

With advances in computing and telecommunications technologies, digital images and video are playing key roles in the present information era. Human face is an important biometric object in image and video databases of surveillance systems. Detecting and locating human faces and facial features in an image or image sequence are important tasks in dynamic environments, such a s videos, where noise conditions, illuminations, locations of subjects and pose can vary significantly from frame to frame. An automated system for human face recognition in real time background for a college to mark the attendance of their employees and students. So Smart Attendance using Real Time Face Recognition is a real world solution which comes with day to day activities of handling employees. Here multiple user faces are detected and recognized with the data base trained multiple texture based features.

EXISTING SYSTEM

The existing system is available in the touch so that the user need to get in touch with the system so that it is tedious process in the home automation devices.

DRAWBACKS

The current implementation is prone to disturbances like noise from surrounding environment, when a person is giving commands to the mirror. As it is mostly used in a single person or small family environment such as our home, it is free from such kind of errors in those scenarios.

PROPOSED SYSTEM

• Face Recognition based User Login
• Voice commands to control user interface and the information to be displayed
• Customized User Experience by providing information about things like Stocks that the user like to see every day, what are his events today.

BLOCK DIAGRAM

SOFTWARE REQUIREMENTS

• Python
• OpenCV
• Numpy

APPLICATIONS

• Smart home applications.
• Public place response devices.
• Automatic ticket dispensers.
• Banking sectors

RESULT

Thus the devices for the smart home is designed and it can be implemented in different application purpose.

REFERENCE

[1] G. Coppini, R. Favilla, et al., “Moving Medical Semeiotics to the DigitalRealm. SEMEOTICONS approach to Face Signs of Cardiometabolic Risk”, in Proc. SUPERHEAL – HEALTHINF, 2014.

[2] T. Weise, J. Gall, et al., “Real time head pose estimation from cosumer depth cameras”, in Annual Symposium of the German Association for Pattern Recognition, 2011.

[3] P. Henriquez, et al. “Head pose tracking for immersive applications”, Proc. ICIP 2014.

[4] M. Jones P. Viola, “Robust Real-Time Face Detection”, International Journal of Computer Vision, 2004. [5] S. Lucey, J. F. Cohn J. M. Saragih, “Deformable Model Fitting by Regularized Landmark Mean-Shift”, Int. J. of Image and Vision Computing, 2011.

[6] N., Huitema, C., Liang, L., et al., “Real-time 3D face tracking based on active appearance model constrained by depth data”, Image and Vision Computing, 2014.

[7] S. Izadi, O. Hilliges, et al., “KinectFusion: Real-Time Dense Surface Mapping and Tracking,” in International Symposium on Mixed and Augmented Reality, 2011