Synthetic face generation under various operational conditions in video surveillance

In still-to-video face recognition (FR), the faces captured with surveillance cameras are matched against reference stills of target individuals enrolled to the system. FR is a challenging problem in video surveillance due to uncontrolled capture conditions (variations in pose, expression, illumination, blur, scale, etc.), and the limited number of reference stills to model target individuals. This paper introduces a new approach to generate multiple synthetic face images per reference still based on camera-specific capture conditions to deal with illumination variations. For each reference still, a diverse set of faces from non-target individuals appearing in the camera viewpoint are selected based on luminance and contrast distortion. These face images are then decomposed into detail layer and large scale layer using an edge-preserving image decomposition to obtain their illumination dependent component. Finally, the large scale layers of these images are morphed with each reference still image to generate multiple synthetic reference stills that incorporate illumination and contrast conditions. Experimental results obtained with the ChokePoint dataset reveal that these synthetic faces produce an enhanced face model. As the number of synthetic faces grows, the proposed approach provides a higher level of accuracy and robustness across a range of capture conditions.