Results 1 - 10
of
32
X.: Fusing robust face region descriptors via multiple metric learning for face recognition
- in the wild. In: Computer Vision and Pattern Recognition (CVPR), IEEE
, 2013
"... In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex ap-pearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face re-gion descriptors. Specifically, we divide each image (resp. video) into ..."
Abstract
-
Cited by 35 (4 self)
- Add to MetaCart
(Show Context)
In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex ap-pearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face re-gion descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted pro-tocol. 1.
Pcanet: A simple deep learning baseline for image classification?” arXiv preprint arXiv:1404.3606
, 2014
"... Abstract — In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal com-ponent analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed ..."
Abstract
-
Cited by 11 (0 self)
- Add to MetaCart
Abstract — In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal com-ponent analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets
Discriminatively trained dense surface normal estimation
- In Proc. of ECCV (2014
"... Abstract. In this work we propose the method for a rather unexplored problem of computer vision- discriminatively trained dense surface nor-mal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the pro ..."
Abstract
-
Cited by 6 (1 self)
- Add to MetaCart
(Show Context)
Abstract. In this work we propose the method for a rather unexplored problem of computer vision- discriminatively trained dense surface nor-mal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding. We apply our method to two challenging data sets containing images of man-made environments, the indoor NYU2 data set and the outdoor KITTI data set. Our surface normal predictor achieves results better than initially expected, significantly outperforming state-of-the-art. 1
Subtasks of Unconstrained Face Recognition
"... Abstract: Unconstrained face recognition remains a challenging computer vision problem despite recent exceptionally high results ( ∼ 95 % accuracy) on the current gold standard evaluation dataset: Labeled Faces in the Wild (LFW) (Huang et al., 2008; Chen et al., 2013). We offer a decomposition of th ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
Abstract: Unconstrained face recognition remains a challenging computer vision problem despite recent exceptionally high results ( ∼ 95 % accuracy) on the current gold standard evaluation dataset: Labeled Faces in the Wild (LFW) (Huang et al., 2008; Chen et al., 2013). We offer a decomposition of the unconstrained problem into subtasks based on the idea that invariance to identity-preserving transformations is the crux of recognition. Each of the subtasks in the Subtasks of Unconstrained Face Recognition (SUFR) challenge consists of a same-different face-matching problem on a set of 400 individual synthetic faces rendered so as to isolate a specific transformation or set of transformations. We characterized the performance of 9 different models (8 previously published) on each of the subtasks. One notable finding was that the HMAX-C2 feature was not nearly as clutter-resistant as had been suggested by previous publications (Leibo et al., 2010; Pinto et al., 2011). Next we considered LFW and argued that it is too easy of a task to continue to be regarded as a measure of progress on unconstrained face recognition. In particular, strong performance on LFW requires almost no invariance, yet it cannot be considered a fair approximation of the outcome of a detection→alignment pipeline since it does not contain the kinds of variability that realistic alignment systems produce when working on non-frontal faces. We offer a new, more difficult, natural image dataset: SUFR-in-the-Wild (SUFR-W), which we created using a protocol that was similar to LFW, but with a few differences designed to produce more need for transformation invariance. We present baseline results for eight different face recognition systems on the new dataset and argue that it is time to retire LFW and move on to more difficult evaluations for unconstrained face recognition. 1
FOR THE DEGREE OF
, 2014
"... Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License. ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License. ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate
Face recognition via archetype hull ranking
- In Proc. ICCV
, 2013
"... The archetype hull model is playing an important role in large-scale data analytics and mining, but rarely applied to vision problems. In this paper, we migrate such a geometric model to address face recognition and verification together through proposing a unified archetype hull ranking frame-work. ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
The archetype hull model is playing an important role in large-scale data analytics and mining, but rarely applied to vision problems. In this paper, we migrate such a geometric model to address face recognition and verification together through proposing a unified archetype hull ranking frame-work. Upon a scalable graph characterized by a compact set of archetype exemplars whose convex hull encompasses most of the training images, the proposed framework ex-plicitly captures the relevance between any query and the stored archetypes, yielding a rank vector over the archetype hull. The archetype hull ranking is then executed on ev-ery block of face images to generate a blockwise similarity measure that is achieved by comparing two different rank vectors with respect to the same archetype hull. After inte-grating blockwise similarity measurements with learned im-portance weights, we accomplish a sensible face similarity measure which can support robust and effective face recog-nition and verification. We evaluate the face similarity mea-sure in terms of experiments performed on three benchmark face databases Multi-PIE, Pubfig83, and LFW, demonstrat-ing its performance superior to the state-of-the-arts. 1.
A Markov Random Field Groupwise Registration Framework for Face Recognition
"... Abstract—In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison. Index Terms—Face recognition, deformable image registration, groupwise registration, Markov random field, correspondences, anatomical signature Ç 1
Learning Compact Binary Face Descriptor for Face Recognition
"... Abstract—Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong p ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors. Index Terms—Face recognition, heterogeneous face matching, feature learning, binary feature, compact feature, biometrics Ç 1
Noname manuscript No. (will be inserted by the editor) Image Based Geo-Localization in the Alps
"... Abstract Given a picture taken somewhere in the world, automatic geo-localization of such an image is an extremely useful task especially for historical and forensic sciences, documentation purposes, organization of the world’s photographs and in-telligence applications. While tremendous progress ha ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract Given a picture taken somewhere in the world, automatic geo-localization of such an image is an extremely useful task especially for historical and forensic sciences, documentation purposes, organization of the world’s photographs and in-telligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural environ-ments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this work, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an automated approach for very large scale visual localization that can efficiently exploit visual information (contours) and geometric constraints (consistent orientation) at the same time. We validate the system at the scale of Switzerland (40000km2) using over 1000 landscape query images with ground truth GPS position.
1Pattern Recognition Letters journal homepage: www.elsevier.com Compact Color-Texture Description for Texture Classification
"... Describing textures is a challenging problem in computer vision and pattern recognition. The clas-sification problem involves assigning a category label to the texture class it belongs to. Several fac-tors such as variations in scale, illumination and viewpoint make the problem of texture descrip-ti ..."
Abstract
- Add to MetaCart
Describing textures is a challenging problem in computer vision and pattern recognition. The clas-sification problem involves assigning a category label to the texture class it belongs to. Several fac-tors such as variations in scale, illumination and viewpoint make the problem of texture descrip-tion extremely challenging. A variety of histogram based texture representations exists in literature. However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors sig-nificantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehen-sive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that com-bining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7.8%, 4.3 % and 5.0 % on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respec-tively. c © 2014 Elsevier Ltd. All rights reserved. 1.
