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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 90 条记录，以下是61-70 订阅

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ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016 29

ChaLearn Looking at People and Faces of the World: Face Anal...

引用

29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Escalera, Sergio Tones, Mercedes Torres Martinez, Brais Baro, Xavier Jair Escalante, Hugo Guyon, Isabelle Tzimiropoulos, Georgios Corneanu, Ciprian Oliu, Marc Bagheri, Mohammad Ali Valstar, Michel Univ Barcelona Barcelona Spain Univ Nottingham Nottingham England Univ Oberta Catalunya Barcelona Spain INAOE Puebla Mexico Univ Paris Saclay Paris France Dalhousie Univ Halifax NS Canada Univ Larestan Larestan Iran

ISBN: (纸本)9781509014378

We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. the first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. this paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions.

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Texture Complexity based Redundant Regions Ranking for Object Proposal 29

Texture Complexity based Redundant Regions Ranking for Objec...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ke, Wei Zhang, Tianliang Chen, Jie Wan, Fang Ye, Qixiang Han, Zhenjun Univ Chinese Acad Sci Beijing Peoples R China Univ Oulu CMV SF-90100 Oulu Finland

ISBN: (纸本)9781509014378

Object proposal has been successfully applied in recent visual object detection approaches and shown improved computational efficiency. the purpose of object proposal is to use as few as regions to cover as many as objects. In this paper, we propose a strategy named Texture Complexity based Redundant Regions Ranking (TCR) for object proposal. Our approach first produces rich but redundant regions using a color segmentation approach, i.e. Selective Search. It then uses Texture Complexity (TC) based on complete contour number and Local Binary pattern (LBP) entropy to measure the objectness score of each region. By ranking based on the TC, it is expected that as many as true object regions are preserved, while the number of the regions is significantly reduced. Experimental results on the PASCAL VOC 2007 dataset show that the proposed TCR significantly improves the baseline approach by increasing AUC (area under recall curve) from 0.39 to 0.48. It also outperforms the state-of-the-art with AUC and uses fewer detection proposals to achieve comparable recall rates.

关键词： Local binary pattern

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Simultaneous Semi-Coupled Dictionary Learning for Matching RGBD data 29

Simultaneous Semi-Coupled Dictionary Learning for Matching R...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Das, Nilotpal Mandal, Devraj Biswas, Soma Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781509014378

Matching with hidden information which is available only during training and not during testing has recently become an important research problem. Matching data from two different modalities, known as cross-modal matching is another challenging problem due to the large variations in the data coming from different modalities. Often, these are treated as two independent problems. But for applications like matching RGBD data, when only one modality is available during testing, it can reduce to either of the two problems. In this work, we propose a framework which can handle both these scenarios seamlessly with applications to matching RGBD data of Lambertian objects. the proposed approach jointly uses the RGB and depth data to learn an illumination invariant canonical version of the objects. Dictionaries are learnt for the RGB, depth and the canonical data, such that the transformed sparse coefficients of the RGB and the depth data is equal to that of the canonical data. Given RGB or depth data, their sparse coefficients corresponding to their canonical version is computed which can be directly used for matching using a Mahalanobis metric. Extensive experiments on three datasets, EURECOM, VAP RGB-D-T and Texas 3D Face recognition database show the effectiveness of the proposed framework.

关键词： Face recognition

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Gaussian Conditional Random Fields for Face recognition 29

Gaussian Conditional Random Fields for Face Recognition

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Smereka, Jonathon M. Kumar, B. V. K. Vijaya Rodriguez, Andres Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA Intel Corp Hillsboro OR 97124 USA

ISBN: (纸本)9781509014378

We propose a Gaussian Conditional Random Field (GCRF) approach to modeling the non-stationary distortions that are introduced from changing facial expressions during acquisition. While previous work employed a Gaussian Markov Random Field (GMRF) to perform deformation tolerant matching of periocular images, we show that the approach is not well-suited for facial images, which can contain significantly larger and more complex deformations across the image. Like the GMRF, the GCRF tries to find the maximum scoring assignment between a match pair in the presence of non-stationary deformations. However, unlike the GMRF, the GCRF directly computes the posterior probability that the observed deformation is consistent with the distortions exhibited in other authentic match pairs. the difference is the inclusion of a derived mapping between an input comparison and output deformation score. We evaluate performance on the CMU Multi-PIE facial dataset across all sessions and expressions, finding that the GCRF is significantly more effective at capturing naturally occurring large deformations than the previous GMRF approach.

关键词： Deformation

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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture recognition 29

ChaLearn Looking at People RGB-D Isolated and Continuous Dat...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wan, Jun Li, Stan Z. Zhao, Yibing Zhou, Shuai Guyon, Isabelle Escalera, Sergio Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China Macau Univ Sci & Technol Macau Peoples R China UPSud Orsay France Univ Paris Saclay Paris France ChaLearn Barcelona Spain Univ Barcelona Comp Vis Ctr E-08007 Barcelona Spain

ISBN: (纸本)9781509014378

In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD) and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset (CGD) that has a total of more than 50000 gestures for the "one-shot-learning" competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences. Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for "user independent" gesture recognition. the first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. the baseline method based on the bag of visual words model is also presented.

关键词： Gesture recognition

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Learning Discriminative Features with Class Encoder 29

Learning Discriminative Features with Class Encoder

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Shi, Hailin Zhu, Xiangyu Lei, Zhen Liao, Shengcai Li, Stan Z. Univ Chinese Acad Sci Chinese Acad Sci Ctr Biometr & Secur Res Beijing Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781509014378

Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one of which the labels are identical. Class-encoder aims to minimize the intra-class variations in the feature space, and to learn a good discriminative manifolds on a class scale. We impose the class-encoder as a constraint into the softmax for better supervised training, and extend the reconstruction on feature-level to tackle the parameter size issue and translation issue. the experiments show that the class-encoder helps to improve the performance on benchmarks of classification and face recognition. this could also be a promising direction for fast training of face recognition models.

关键词： Face recognition

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Facial Expression recognition from World Wild Web 29

Facial Expression Recognition from World <i>Wild</i> Web

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mollahosseini, Ali Hassani, Behzad Salvador, Michelle J. Abdollahi, Hojjat Chan, David Mahoor, Mohammad H. Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA Univ Denver Dept Comp Sci Denver CO USA

ISBN: (纸本)9781509014378

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. the World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. this paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? the results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%.

关键词： Deep neural networks

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Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models 29

Apparent Age Estimation from Face Images Combining General a...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Antipov, Grigory Baccouche, Moez Berrani, Sid-Ahmed Dugelay, Jean-Luc France Telecom Orange Labs 4 Rue Clos Courtel F-35512 Cesson Sevigne France Eurecom 450 Route Chappes F-06410 Biot France

ISBN: (纸本)9781509014378

this work describes our solution in the second edition of the ChaLearn LAP competition on Apparent Age Estimation. Starting from a pretrained version of the VGG-16 convolutional neural network for face recognition, we train it on the huge IMDB-Wiki dataset for biological age estimation and then fine-tune it for apparent age estimation using the relatively small competition dataset. We show that the precise age estimation of children is the cornerstone of the competition. therefore, we integrate a separate "children" VGG- 16 network for apparent age estimation of children between 0 and 12 years old in our final solution. the "children" network is fine-tuned from the "general" one. We employ different age encoding strategies for training "general" and "children" networks: the soft one (label distribution encoding) for the "general" network and the strict one (0/1 classification encoding) for the "children" network. Finally, we highlight the importance of the state-of-the-art face detection and face alignment for the final apparent age estimation. Our resulting solution wins the 1st place in the competition significantly outperforming the runner-up.

关键词： Face recognition

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Weakly Supervised Facial Analysis with Dense Hyper-column Features 29

Weakly Supervised Facial Analysis with Dense Hyper-column Fe...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Chenchen Zheng, Yutong Khoa Luu Hoang Ngan Le, T. Bhagavatula, Chandrasekhar Savvides, Marios Carnegie Mellon Univ CyLab Biometr Ctr Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781509014378

Weakly supervised methods have recently become one of the most popular machine learning methods since they are able to be used on large-scale datasets without the critical requirement of richly annotated data. In this paper, we present a novel, self-taught, discriminative facial feature analysis approach in the weakly supervised framework. Our method can find regions which are discriminative across classes yet consistent within a class and can solve many face related problems. the proposed method first trains a deep face model with high discriminative capability to extract facial features. the hypercolumn features are then used to give pixel level representation for better classification performance along with discriminative region detection. In addition, calibration approaches are proposed to enable the system to deal with multi-class and mixed-class problems. the system is also able to detect multiple discriminative regions from one image. Our uniform method is able to achieve competitive results in various face analysis applications, such as occlusion detection, face recognition, gender classification, twins verification and facial attractiveness analysis.

关键词： Face recognition

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Sequential Face Alignment via Person-Specific Modeling in the Wild 29

Sequential Face Alignment via Person-Specific Modeling in th...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Peng, Xi Huang, Junzhou Metaxas, Dimitris N. Rutgers State Univ Piscataway NJ 08854 USA Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781509014378

Sequential face alignment, in essence, deals with non-rigid deformation that changes over time. Although numerous methods have been proposed to show impressive success on still images, many of them still suffer from limited performance when it comes to sequential alignment in wild scenarios, e.g., involving large pose/expression variations and partial occlusions. the underlying reason is that they usually perform sequential alignment by independently applying models trained offline in each frame in a tracking-by-detection manner but completely ignoring temporal constraints that become available in sequence. To address this issue, we propose to exploit incremental learning for person-specific alignment. Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame. More importantly, it incrementally updates the representation sub-space and simultaneously adapts the cascade regressors in parallel using a unified framework. Person-specific modeling is eventually achieved on the fly while the drifting issue is significantly alleviated by erroneous detection using both part and holistic descriptors. Extensive experiments on both controlled and in-the-wild datasets demonstrate the superior performance of our approach compared with the state of the arts in terms of fitting accuracy and efficiency.

关键词： computer vision

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