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WLD: A Robust Local Image Descriptor
  • J. Chen, S. Shan, +4 authors W. Gao
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 September 2010
Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT), and experimental results on human face detection also show a promising performance comparable to the best known results onThe MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set. Expand
Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition
A novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. Expand
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
The evaluation protocol based on the CAS-PEAL-R1 database is discussed and the performance of four algorithms are presented as a baseline to do the following: elementarily assess the difficulty of the database for face recognition algorithms; preference evaluation results for researchers using the database; and identify the strengths and weaknesses of the commonly used algorithms. Expand
Deep Supervised Hashing for Fast Image Retrieval
A novel Deep Supervised Hashing method to learn compact similarity-preserving binary code for the huge body of image data and extensive experiments show the promising performance of the method compared with the state-of-the-arts. Expand
Manifold-Manifold Distance with application to face recognition based on image set
The proposed MMD method outperforms the competing methods on the task of Face Recognition based on Image Set, and a novel manifold learning approach is proposed, which expresses a manifold by a collection of local linear models, each depicted by a subspace. Expand
Multi-View Discriminant Analysis
This work proposes a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Expand
Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment
This paper proposes a Coarse-to-Fine Auto-encoder Networks (CFAN) approach, which cascades a few successive Stacked Auto- Encoding Networks (SANs) so that the first SAN predicts the landmarks quickly but accurately enough as a preliminary, by taking as input a low-resolution version of the detected face holistically. Expand
AttGAN: Facial Attribute Editing by Only Changing What You Want
The proposed method is extended for attribute style manipulation in an unsupervised manner and outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved. Expand
Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition
The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate. Expand
Manifold Discriminant Analysis
The proposed MDA method is evaluated on the tasks of object recognition with image sets, including face recognition and object categorization, and seeks to learn an embedding space, where manifolds with different class labels are better separated, and local data compactness within each manifold is enhanced. Expand