Face Recognition Robust to Large Pose Angle from one Gallery
This event took place on Wednesday 12 September 2007 at 11:30
Dr. Ting Shan National ICT Australia
In recent years, the use of Intelligent Closed-Circuit Television (ICCTV) for crime prevention and detection has attracted significant attention. Existing face recognition systems require passport-quality photos to achieve good performance. However, use of CCTV images is much more problematic due to large variations in illumination, facial expressions and pose angle. In this talk we present our approach of a pose variability compensation technique, which synthesizes realistic frontal face images from non-frontal views. It is based on modelling the face via Active Appearance Models and detecting the pose through a correlation model. The proposed technique is coupled with Adaptive Principal Component Analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. Experiments on the FERET dataset show up to 6 fold performance improvements.
This event took place on Wednesday 12 September 2007 at 11:30
In recent years, the use of Intelligent Closed-Circuit Television (ICCTV) for crime prevention and detection has attracted significant attention. Existing face recognition systems require passport-quality photos to achieve good performance. However, use of CCTV images is much more problematic due to large variations in illumination, facial expressions and pose angle. In this talk we present our approach of a pose variability compensation technique, which synthesizes realistic frontal face images from non-frontal views. It is based on modelling the face via Active Appearance Models and detecting the pose through a correlation model. The proposed technique is coupled with Adaptive Principal Component Analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. Experiments on the FERET dataset show up to 6 fold performance improvements.
Future Internet
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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