Multi-Modal Tensor Face for Simultaneous Super-Resolution and Recognition

Face images of non-frontal views under poor illumination with low resolution reduce dramatically face recognition accuracy. This is evident most compellingly by the very low recognition rate of all existing face recognition systems when applied to live CCTV camera input. In this paper, we present a Bayesian framework to perform multimodal (such as variations in viewpoint and illumination) face image super-resolution for recognition in tensor space. Given a single modal low-resolution face image, we benefit from the multiple factor interactions of training tensor, and super-resolve its high-resolution reconstructions across different modalities for face recognition. Instead of performing pixel-domain super-resolution and recognition independently as two separate sequential processes, we integrate the tasks of super-resolution and recognition by directly computing a maximum likelihood identity parameter vector in high-resolution tensor space for recognition. We show results from multi-m...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Kui Jia, Shaogang Gong
• Keywords: Computer Vision, Face Recognition Accuracy, Face Recognition Systems, ICCV 2005, Low Recognition Rate, Modal Low-resolution Face, Recognition Rates