Access Control Via Face Recognition

Group Members  Thilanka Priyankara  Vimalaharan Paskarasundaram  Manosha Silva  Dinusha Perera

Introduction  What is Recognition ?  Can you recognize this guy?  Face is belonged to the different guy.  You will feel because your brain is trained to track such things.

Who is this?

Types of Access Control Recognition  Finger Print  Shape of Ear  Retina Recognition  Face Recognition

Advantages of Facial Recognition  Give more percentage of guaranty compared to other techniques.  Intruders should take more effort to get into the system. (Making a Mask)  With another mechanism, system can be made to more secure

What we are going to do?  Access Control System  2 way access mechanism 1.Bar code 2.Face Recognition  If both mechanisms are satisfied only user can enter the implemented area

Facial Recognition  We use 1.Computer Vision 2.Neural Networks 3.??????????????

Without Neural Network  If straight forward matching is used  Noise can be there and different lighting  If user comes with a moustache which he didn’t have before 2 weeks will not be allowed to get in Ooh should I have shave my moustache to get in??

Why Neural Network?  Adaptive-learning When users training the system it will keep on learning about the domain.  Self-organization It gathers the knowledge and organize it by itself  Fault-tolerance capabilities It can tolerate the noise small variations These features are very much needed to do a system which can do the human like recognizing

Techniques Face recognition uses mainly the following techniques:  Facial geometry: uses geometrical characteristics of the face. May use several cameras to get better accuracy (2D, 3D...)  Skin pattern recognition (Visual Skin Print)  Facial thermogram: uses an infrared camera to map the face temperatures  Smile: recognition of the wrinkle changes when smiling

Facial Geometry  When searching for the facial features in the face image, Look for high level features first  E.g. eyes, nose, mouth Then relative to high level features look at the positions of low level features  E.g. eyebrows

Problems faced, Identifying a Face  find the face in the image Face finding solves the important task of making face recognition translation  Scaling of the face image  Distance from the camera  Rotation of the face image if needed  Lighting conditions at the time the picture is taken

Conclusion  Not easy  Neural network training will eat the time

Conclusion cont…  Core technologies are highly researched  Facial scan has unique advantages over other biometrics  Automated facial detection and facial recognition algorithm are not yet mature  Facial-recognition systems create opportunities to identify people unobtrusively