Face Hallucination: Theory and Practice
International Journal of Computer Vision
Algorithms for sparse nonnegative tucker decompositions
Neural Computation
A unified tensor framework for face recognition
Pattern Recognition
IEEE Transactions on Neural Networks
Coupled Metric Learning for Face Recognition with Degraded Images
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Recovering facial intrinsic images from a single input
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Robust low-resolution face identification and verification using high-resolution features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A super-resolution based method to synthesize visual images from near infrared
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficient face recognition using tensor subspace regression
Neurocomputing
Thorax biometrics from millimetre-wave images
Pattern Recognition Letters
Optimum subspace learning and error correction for tensors
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Face relighting based on multi-spectral quotient image and illumination tensorfaces
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Decimation estimation and super-resolution using zoomed observations
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Remote identification of faces: Problems, prospects, and progress
Pattern Recognition Letters
Boosting with side information
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Feature-domain super-resolution for iris recognition
Computer Vision and Image Understanding
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
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 multi-modal (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-modal super-resolution and face recognition experiments across different imaging modalities, using low-resolution images as testing inputs and demonstrate improved recognition rates over standard tensorface and eigenface representations.