Invited paper: Embedded face and biometric technologies for national and border security (original) (raw)
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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" recognition, in public spaces such as mass transit centres. 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-offeatures techniques generally attain better performance, with significantly lower computational loads. The histogram-based 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.
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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 surveil-lance system is “face in the crowd ” recognition, in public spaces such as mass transit centres. 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 mod-ifications. 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 ...
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Large space with many cameras require huge storage and computational power to process these data for surveillance applications. In this paper we propose a distributed camera and processing based face detection and recognition system which can generate information for finding spatiotemporal movement pattern of individuals over a large monitored space. The system is built upon Hadoop Distributed File System using map reduce programming model. A novel key generation scheme using distance based hashing technique has been used for distribution of the face matching task. Experimental results have established effectiveness of the technique.
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Security is a major aspect of all public places. There are numerous measures including security personnel , cameras and live footages that are continuously monitored. When it, comes to security, the response time to a situation is very important and it is directly dependent on the time taken to detect a threat. The current security systems are largely dependent on in-person monitoring at all times and are prone to human errors and inefficient timing. The purpose of this paper is to propose a system which removes the need for in person monitoring and improves the efficiency in automatic surveillance. We make use of Raspberry Pi boards attached for each camera, hence the cameras have the ability to work as standalone system or as an interconnected network of cameras forming a surveillance system. A specific server is located at the centre of our system and is called a Controller node. The central node receives the videos or images and performs facial recognition and triggers the alert systems on detecting threats. A card sized Raspberry Pi with image processing capabilities through the use of Open CV and with control algorithms used for facial detection and recognition, identifies the suspect and displays the images to concerned client at the security room and alerts the predefined contact number with a call and a text message.
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The Research Network for a Secure Australia (RNSA) is a multidisciplinary collaboration established to strengthen Australia's research capacity for protecting critical infrastructure (CIP) from natural or human caused disasters including terrorist acts. The RNSA facilitates a knowledge-sharing network for research organisations, government and the private sector to develop research tools and methods to mitigate emerging safety and security issues relating to critical infrastructure. World-leaders with extensive national and international linkages in relevant scientific, engineering and technological research will lead this collaboration. The RNSA also organises various activities to foster research collaboration and nurture young investigators.
Integrating face recognition into security systems
Lecture Notes in Computer Science, 1997
Automated processing of facial images has become a serious market for both hard-and software products. For the commercial success of face recognition systems it is most crucial that the process of face image capturing is very convenient for the people exposed to such systems. As a consequence, the whole imaging setup has to be carefully designed for each operational scenario. The paper mainly deals with the problem of face image capturing in real security applications. In this context, we use the term face recognition in a broader sense, including the important functionality of face spotting followed by face validation. The latter provides the front-end of videobased automated person authentication. We describe two examples for a successful integration of face recognition in a security system. One is a system for recognizing authorization to use a vehicle and the other one is an automatic assistant" for a security desk o cer. Both applications require techniques for face detection and face segmentation. Key problems are the camera setup and the size of the surveillance area. We propose an approach using a tracking camera with a high speed pan-tilt unit.