Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Local Binary Patterns as an Image Preprocessing for Face Authentication
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Face recognition from a single image per person: A survey
Pattern Recognition
Geometric invariants for 2D/3D face recognition
Pattern Recognition Letters
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A survey of 3d face recognition methods
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Face matching between near infrared and visible light images
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
3D aided face recognition across pose variations
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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In the recent years, 3D Face recognition has emerged as a major solution to deal with the unsolved issues for reliable 2D face recognition, i.e. lighting condition and viewpoint variations. However, 3D method is currently limited by its registration and computation cost. In this paper, we propose to investigate a solution named asymmetric face recognition scheme, enrolling people in 3D environment but performing identification in 2D. The goal is to limit the use of 3D data to where it really helps to improve recognition performances. In our approach, Local Binary Patterns (LBP) is used as an efficient facial representation for both 2D texture images and 3D range images. A weighted Chi square distance is used as matching score between the 2D LBP facial representations; Canonical Correlation Analysis (CCA) is applied to learn the mapping between LBP-based range face images (3D) and LBP facial texture images (2D). Both matching scores are further fused to obtain the final result. Compared with the traditional 2D/2D algorithms, the proposed asymmetric face recognition scheme achieves better accuracy; while avoiding the high cost of data acquisition and computation in 3D/3D approaches.