We change your Security Challenges with Integrated IP Solutions
Inteligence
Cygnetic (Pty) Ltd not only provide you the best design in IP CCTV Systems, but add inteligence to your HDTV system using the best of breed of integrated solutions that cover all verticals of the security market.
Solutions
Cygnetic (Pty) Ltd offer integrated solutions like LPR (Number Plate Recognition), Facial Recognition, People Counting, Vehicle Traffic Management and Counting, Heat Mapping, Access Control, Video Forensics, etc. all integrated into one common OPEN Platform.
Abstract
CCTV surveillance systems have long been promoted as being effective in improving public safety. However due to the amount of cameras installed, many sites have abandoned expensive human monitoring and only record video for forensic purposes. One of the sought-after capabilities of an automated surveillance system is "face in the crowd"; facial recognition, in public spaces such as shopping centres and tourest destinations. Apart from accuracy and robustness to nuisance factors such as pose variations, in such surveillance situations the other important factors are scalability and fast performance. We evaluate recent approaches to the recognition of faces at large pose angles from a gallery of frontal images and propose novel adaptations as well as modifications. We compare and contrast the accuracy, robustness and speed of an Active Appearance Model (AAM) based method (where realistic frontal faces are synthesized from non-frontal probe faces) against bag-of-features methods. We show a novel approach where the performance of the AAM based technique is increased by side-stepping the image synthesis step, also resulting in a considerable speedup.
Additionally, we adapt a histogram-based bag-of-features technique to face classification and contrast its properties to a previously proposed direct bag-of-features method. We further show that the two bag-of-features approaches can be considerably sped up, without a loss in classification accuracy, via an approximation of the exponential function. Experiments on the FERET and PIE databases suggest that the bag-of-features techniques generally attain better performance, with significantly lower computational loads. The histogrambased bag-of-features technique is capable of achieving an average recognition accuracy of 89% for pose angles of around 25 degrees. Finally, we provide a discussion on implementation as well as legal challenges surrounding research on automated surveillance.
