Video Processing and Communications
Video Processing and Communications
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Generating frontal view face image for pose invariant face recognition
Pattern Recognition Letters
On transforming statistical models for non-frontal face verification
Pattern Recognition
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Fusion of Qualities for Frame Selection in Video Face Verification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Describable Visual Attributes for Face Verification and Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
An associate-predict model for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Face verification using large feature sets and one shot similarity
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
3D model-based face recognition in video
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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High-quality still-to-still (image-to-image) face authentication has shown success under controlled conditions in many safety applications. However, video-to-video face authentication is still challenging due to appearance variations caused by pose changes. In this paper, we propose a video-to-video face authentication system that is robust to pose variations by making use of synthesized frontal face appearance that contains both texture and shape information. To obtain the appearance, we first reconstruct 3D face shape from face feature points detected from the video using active shape model (ASM). Conventional ASM algorithms cannot handle large pose variations and fast head movement exhibited in video sequences. To address these problems, we present a novel prediction-assisted approach that is capable of providing an accurate shape initiation as well as automatically switching on multi-view models for ASM. Then we can generate frontal shape mesh from the reconstructed 3D face shape. Based on the mesh, we synthesize frontal face appearance with the ASM-detected faces in video. For authentication, in order to match the synthesized appearances of enrollment and probe, we propose a 2-directional 2-dimensional client specific fisher's linear discriminant algorithm. The proposed algorithm is a variant of fisher's linear discriminant (FLD) and directly computes eigenvectors of image scatter matrices in row and column direction without matrix-to-vector conversion. In experiments, our authentication system is compared with the other state-of-art approaches on public face database and our face database. The results show that our system demonstrates a higher authentication accuracy and pose-robust performance.