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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 654 条记录，以下是211-220 订阅

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What is a 'Good' Periocular Region for recognition?

What is a 'Good' Periocular Region for Recognition?

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Smereka, Jonathon M. Kumar, B. V. K. Vijaya

ISBN: (纸本)9780769549903

In challenging image acquisition settings where the performance of iris recognition algorithms degrades due to poor segmentation of the iris, image blur, specular reflections, and occlusions from eye lids and eye lashes, the periocular region has been shown to offer better recognition rates. However, the definition of a periocular region is subject to interpretation. this paper investigates the question of what is the best periocular region for recognition by identifying sub-regions of the ocular image when using near-infrared (NIR) or visible light (VL) sensors. To determine the best periocular region, we test two fundamentally different algorithms on challenging periocular datasets of contrasting build on four different periocular regions. Our results indicate that system performance does not necessarily improve as the ocular region becomes larger. Rather in NIR images the eye shape is more important than the brow or cheek as the image has little to no skin texture (leading to a smaller accepted region), while in VL images the brow is very important (requiring a larger region).

关键词： image restoration image segmentation image sensors iris recognition

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Probabilistic Elastic Matching for Pose Variant Face Verification

Probabilistic Elastic Matching for Pose Variant Face Verific...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Haoxiang Hua, Gang Lin, Zhe Brandt, Jonathan Yang, Jianchao Stevens Inst Technol Hoboken NJ 07030 USA Adobe Syst Inc San Jose CA 95110 USA

ISBN: (纸本)9780769549897

Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic matching method. We take a part based representation by extracting local features (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each feature with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of all face images in the training corpus. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms. Each Gaussian component builds correspondence of a pair of features to be matched between two faces/face tracks. For face verification, we train an SVM on the vector concatenating the difference vectors of all the feature pairs to decide if a pair of faces/face tracks is matched or not. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that our method outperforms the state-of-the-art in the most restricted protocol on Labeled Face in the Wild (LFW) and the YouTube video face database by a significant margin.

关键词： Gaussian processes belief networks face recognition feature extraction image matching probability support vector machines

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Deep Convolutional Network Cascade for Facial Point Detection

Deep Convolutional Network Cascade for Facial Point Detectio...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9780769549897

We propose a new approach for estimation of the positions of facial keypoints with three-level carefully designed convolutional networks. At each level, the outputs of multiple networks are fused for robust and accurate estimation. thanks to the deep structures of convolutional networks, global high-level features are extracted over the whole face region at the initialization stage, which help to locate high accuracy keypoints. there are two folds of advantage for this. First, the texture context information over the entire face is utilized to locate each keypoint. Second, since the networks are trained to predict all the keypoints simultaneously, the geometric constraints among keypoints are implicitly encoded. the method therefore can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. the networks at the following two levels are trained to locally refine initial predictions and their inputs are limited to small regions around the initial predictions. Several network structures critical for accurate and robust facial point detection are investigated. Extensive experiments show that our approach outperforms state-of-the- art methods in both detection accuracy and reliability(1).

关键词： face recognition feature extraction hidden feature removal image texture pose estimation

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Evaluating Open-Universe Face Identification on the Web

Evaluating Open-Universe Face Identification on the Web

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Becker, Brian C. Ortiz, Enrique G. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9780769549903

Face recognition is becoming a widely used technique to organize and tag photos. Whether searching, viewing, or organizing photos on the web or in personal photo albums, there is a growing demand to index real-world photos by the subjects in them. Even consumer platforms such as Google Picasa, Microsoft Photo Gallery, and social network sites such as Facebook have integrated forms of automated face tagging and recognition;furthermore, a number of libraries and cloud-based APIs that perform face recognition have become available. With such a plethora of choices, comparisons of recent advances become more important to gauge the state of progress in the field. this paper evaluates face identification in the context of not only research algorithms, but also considers consumer photo products, client-side libraries, and cloud-based APIs on a new, large-scale dataset derived from PubFig83 and LFW in a realistic open-universe scenario.

关键词： application program interfaces cloud computing face recognition

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Edge Enhanced Depth Motion Map for Dynamic Hand Gesture recognition

Edge Enhanced Depth Motion Map for Dynamic Hand Gesture Reco...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Chenyang Tian, Yingli CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9780769549903

In this paper, we propose a novel approach to recognize dynamic hand gestures from depth video by integrating Edge Enhanced Depth Motion Map together with Histogram of Gradient descriptor. the novelty of this paper has two aspects: first, we propose a novel feature representation, Edge Enhanced Depth Motion Map (E2DMM), balancing the information weighing between shape and motion, which is more suitable for hand gesture recognition;second, we further employ a dynamic temporal pyramid to segment the depth video sequence to address temporal structure information of dynamic hand gestures. Histogram of Gradient is applied on E2DMM to generate vectored representation. Comparison study has been conducted with the state-of-the-art approaches and demonstrates that our approach can achieve better and more stable performance while keeping a relative simple model with lower complexity as well as higher generality.

关键词： edge detection feature extraction gesture recognition image motion analysis image representation image segmentation image sequences statistical distributions video signal processing

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Action recognition with Temporal Relationships

Action Recognition with Temporal Relationships

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Guangchun Wan, Yiwen Santiteerakul, Wasana Tang, Shijun Buckles, Bill P. Univ N Texas Denton TX 76203 USA

ISBN: (纸本)9780769549903

Action recognition is an important component in human-machine interactive systems and video analysis. Besides low-level actions, temporal relationships are also important for many actions, which are not fully studied for recognizing actions. We model the temporal structure of low-level actions based on dense trajectory groups. Trajectory groups are a higher level and more meaningful representation of actions than raw individual trajectories. Based on the temporal ordering of trajectory groups, we describe the temporal structure using Allen's temporal relations in a discriminative manner, and combine it with a generative model using bag-of-words. the simple idea behind the model is to extract mid-level features from domain-independent dense trajectories and classify the actions by exploring the temporal structure among them based on a set of Allen's relations. We compare the proposed approach with bag-of-words representation using public datasets, and the results show that our approach improves recognition accuracy.

关键词： feature extraction image recognition image representation

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LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification

LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ptucha, Raymond Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780769549903

Sparse representations have successfully been exploited for the development of highly accurate classifiers. Unfortunately, these classifiers are computationally intensive and subject to the adverse effects of coefficient contamination, where for example variations in pose may affect identity and expression recognition. We propose a technique, called LGE-KSVD, that addresses both problems and attains state-of-the-art results for face and gesture classification problems. Specifically, LGE-KSVD utilizes variants of Linear extension of Graph Embedding to optimize K-SVD, an iterative technique for small yet overcomplete dictionary learning. the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based linear classifier are jointly learned through LGE-KSVD. the atom optimization process is redefined to have variable support using graph embedding techniques to produce a more flexible and elegant dictionary learning algorithm. Results are obtained for a wide variety of facial and activity recognition problems to demonstrate the robustness of the proposed method.

关键词： dictionaries face recognition graph theory image classification image representation learning (artificial intelligence) object recognition optimisation singular value decomposition

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Maximum Cohesive Grid of Superpixels for Fast Object Localization

Maximum Cohesive Grid of Superpixels for Fast Object Localiz...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Liang Feng, Wei Wan, Liang Zhang, Jiawan Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Tianjin 300072 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China

ISBN: (纸本)9780769549897

this paper addresses a challenging problem of regularizing arbitrary superpixels into an optimal grid structure, which may significantly extend current low-level vision algorithms by allowing them to use superpixels (SPs) conveniently as using pixels. For this purpose, we aim at constructing maximum cohesive SP-grid, which is composed of real nodes, i.e. SPs, and dummy nodes that are meaningless in the image with only position-taking function in the grid. For a given formation of image SPs and proper number of dummy nodes, we first dynamically align them into a grid based on the centroid localities of SPs. We then define the SP-grid coherence as the sum of edge weights, with SP locality and appearance encoded, along all direct paths connecting any two nearest neighboring real nodes in the grid. We finally maximize the SP-grid coherence via cascade dynamic programming. Our approach can take the regional objectness as an optional constraint to produce more semantically reliable SP-grids. Experiments on object localization show that our approach outperforms state-of-the-art methods in terms of both detection accuracy and speed. We also find that with the same searching strategy and features, object localization at SP-level is about 100-500 times faster than pixel-level, with usually better detection accuracy.

关键词： computer vision dynamic programming object detection

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Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers

Adapting a Pedestrian Detector by Boosting LDA Exemplar Clas...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Xu, Jiaolong Vazquez, David Ramos, Sebastian Lopez, Antonio M. Ponsa, Daniel Autonomous Univ Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549903

Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. the results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.

关键词： computer vision object detection pedestrians statistical analysis

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General Regression and Representation Model for Face recognition

General Regression and Representation Model for Face Recogni...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Qian, Jianjun Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9780769549903

Recently, the regularized coding-based classification method (e. g. SRC and CRC) shows a great potential for face recognition. However, most existing coding methods ignore the statistical information from the training data, which actually plays an important role in classification. To address this problem, we develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also introduces the prior information and the specific information to enhance the classification performance. In GRR, we combine the leave-one-out strategy with K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using the iterative algorithm to update the feature weights of the test sample. Finally, we classify the test sample based on the reconstruction error of each class. the proposed model is evaluated on public face image databases. And the experimental results demonstrate the advantages of GRR over state-of-the-art methods.

关键词： face recognition image classification image coding image reconstruction image representation iterative methods regression analysis visual databases

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