Comparison of face matching techniques under pose variation
Proceedings of the 6th ACM international conference on Image and video retrieval
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eye localization for face matching: is it always useful and under what conditions?
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A discriminant analysis for undersampled data
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Combining Facial Skin Mark and Eigenfaces for Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Hierarchical ensemble of global and local classifiers for face recognition
IEEE Transactions on Image Processing
Facial marks: soft biometric for face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fusing local patterns of gabor magnitude and phase for face recognition
IEEE Transactions on Image Processing
Face matching and retrieval using soft biometrics
IEEE Transactions on Information Forensics and Security
Face recognition based on the multi-scale local image structures
Pattern Recognition
Weighted principal component extraction with genetic algorithms
Applied Soft Computing
Explicit integration of identity information from skin regions to improve face recognition
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Fusing magnitude and phase features for robust face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
| Hi-index | 0.00 |
Human faces manifest distinct structures and characteristics when observed in different scales. Traditional face recognition techniques mainly rely on low-resolution face images, leading to the lost of significant information contained in the microscopic traits. In this paper, we introduce a multilayer framework for high resolution face recognition exploiting features in multiple scales. Each face image is factorized into four layers: global appearance, facial organs, skins, and irregular details. We employ Multilevel PCA followed by Regularized LDA to model global appearance and facial organs. However, the description of skin texture and irregular details, for which conventional vector representation are not suitable, brings forth the need of developing novel representations. To address the issue, Discriminative Multiscale Texton Features and SIFT-Activated Pictorial Structure are proposed to describe skin and subtle details respectively. To effectively combine the information conveyed by all layers, we further design an metric fusion algorithm adaptively placing emphasis onto the highly confident layers. Through systematic experiments, we identify different roles played by the layers and convincingly show that by utilizing their complementarities, our framework achieves remarkable performance improvement.