Results 1 - 10
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77
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
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Cited by 35 (4 self)
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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.
B.: Local fisher discriminant analysis for pedestrian re-identification
- In: CVPR
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X.: Locally aligned feature transforms across views
- In: IEEE International Conference on Computer Vision and Pattern Recognition
, 2013
"... In this paper, we propose a new approach for match-ing images observed in different camera views with com-plex cross-view transforms and apply it to person re-identification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross ..."
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Cited by 29 (2 self)
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In this paper, we propose a new approach for match-ing images observed in different camera views with com-plex cross-view transforms and apply it to person re-identification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. The visual fea-tures of an image pair from different views are first lo-cally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering cross-view transforms. They are jointly learned by utilizing sparsity-inducing norm and information theoretical regularization. This approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the state-of-the-art metric learning and person re-identification methods show the superior performance of our approach. 1.
Discriminative deep metric learning for face verification in the wild
- In Proc. CVPR
, 2014
"... This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verifica-tion methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class ..."
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Cited by 20 (2 self)
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This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verifica-tion methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, simultaneously, the proposed D-DML trains a deep neural network which learns a set of hi-erarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold, respectively, so that discriminative information can be ex-ploited in the deep network. Our method achieves very com-petitive face verification performance on the widely used LFW and YouTube Faces (YTF) datasets. 1.
Learning mid-level filters for person re-identification
- in Proc. CVPR
, 2014
"... In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clus-ters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for iden-ti ..."
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Cited by 18 (1 self)
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In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clus-ters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for iden-tifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discrim-inative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchi-cal clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are in-tegrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%. 1.
Deepreid: Deep filter pairing neural network for person re-identification
- In CVPR
, 2014
"... Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detec-tors. Challenges are presented in the form of complex varia-tions of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter ac ..."
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Cited by 17 (3 self)
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Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detec-tors. Challenges are presented in the form of complex varia-tions of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment in-troduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection. In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photomet-ric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperat-ing with others. In contrast to existing works that use hand-crafted features, our method automatically learns features optimal for the re-identification task from data. The learned filter pairs encode photometric transforms. Its deep archi-tecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13,164 images of 1,360 pedes-trians. Unlike existing datasets, which only provide manu-ally cropped pedestrian images, our dataset provides au-tomatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset. 1.
Person Re-Identification using Kernel-based Metric Learning Methods
"... Abstract. Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and ex-tensively evaluate the performance, of four alternatives for re-ID clas-sification: ..."
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Cited by 15 (0 self)
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Abstract. Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and ex-tensively evaluate the performance, of four alternatives for re-ID clas-sification: regularized Pairwise Constrained Component Analysis, ker-nel Local Fisher Discriminant Analysis, Marginal Fisher Analysis and a ranking ensemble voting scheme, used in conjunction with different sizes of sets of histogram-based features and linear, χ2 and RBF-χ2 kernels. Comparisons against the state-of-art show significant improvements in performance measured both in terms of Cumulative Match Characteris-tic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets. 1
Salient Color Names for Person Re-identification
"... Abstract. Color naming, which relates colors with color names, can help people with a semantic analysis of images in many computer vision applications. In this paper, we propose a novel salient color names based color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guara ..."
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Cited by 13 (3 self)
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Abstract. Color naming, which relates colors with color names, can help people with a semantic analysis of images in many computer vision applications. In this paper, we propose a novel salient color names based color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guarantee that a higher probability will be assigned to the color name which is nearer to the color. Based on SCNCD, color distributions over color names in different color spaces are then ob-tained and fused to generate a feature representation. Moreover, the effect of background information is employed and analyzed for person re-identification. With a simple metric learning method, the proposed approach outperforms the state-of-the-art performance (without user’s feedback optimization) on two challenging datasets (VIPeR and PRID 450S). More importantly, the proposed feature can be obtained very fast if we compute SCNCD of each color in advance.
Reference-Based Person Re-Identification
"... Person re-identification refers to recognizing people across non-overlapping cameras at different times and lo-cations. Due to the variations in pose, illumination con-dition, background, and occlusion, person re-identification is inherently difficult. In this paper, we propose a reference-based met ..."
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Cited by 13 (4 self)
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Person re-identification refers to recognizing people across non-overlapping cameras at different times and lo-cations. Due to the variations in pose, illumination con-dition, background, and occlusion, person re-identification is inherently difficult. In this paper, we propose a reference-based method for across camera person re-identification. In the training, we learn a subspace in which the correlations of the reference data from different cameras are maximized using Regularized Canonical Correlation Analysis (RCCA). For re-identification, the gallery data and the probe data are projected into the RCCA subspace and the reference de-scriptors (RDs) of the gallery and probe are constructed by measuring the similarity between them and the reference data. The identity of the probe is determined by comparing the RD of the probe and the RDs of the gallery. Experiments on benchmark dataset show that the proposed method out-performs the state-of-the-art approaches. 1.
Semi-supervised multifeature learning for person re-identification
- In IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS
, 2013
"... Abstract Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically design ..."
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Cited by 11 (7 self)
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Abstract Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary stateof-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem.
