Results 11 - 20 of 320

Accurate face alignment using shape constrained Markov network

by Lin Liang, Fang Wen, Ying-qing Xu, Xiaoou Tang, Heung-yeung Shum - In CVPR , 2006
"... In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network mo ..."
Abstract - Cited by 18 (0 self) - Add to MetaCart
In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network

Learning Face Localization using Hierarchical Recurrent Networks

by Sven Behnke - In Proceedings of ICANN 2002, volume 2415 of LNCS , 2002
"... One of the major parts in human-computer interface applications, such as face recognition and video-telephony, consists in the exact localization of a face in an image. Here, we propose to use hierarchical neural networks with local recurrent connectivity to solve this task, even in presence of comp ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
One of the major parts in human-computer interface applications, such as face recognition and video-telephony, consists in the exact localization of a face in an image. Here, we propose to use hierarchical neural networks with local recurrent connectivity to solve this task, even in presence

FaceTracer: A Search Engine for Large Collections of Images with Faces

by Neeraj Kumar, Peter Belhumeur, Shree Nayar
"... Abstract. We have created the first image search engine based entirely on faces. Using simple text queries such as “smiling men with blond hair and mustaches, ” users can search through over 3.1 million faces which have been automatically labeled on the basis of several facial attributes. Faces in o ..."
Abstract - Cited by 77 (4 self) - Add to MetaCart
in our database have been extracted and aligned from images downloaded from the internet using a commercial face detector, and the number of images and attributes continues to grow daily. Our classification approach uses a novel combination of Support Vector Machines and Adaboost which exploits

Real-time View-based Face Alignment using Active Wavelet Networks

by Changbo Hu , Rogerio Feris, Matthew Turk , 2003
"... The Active Wavelet Network (AWN) [9] approach was recently proposed for automatic face alignment, showing advantages over Active Appearance Models (AAM), such as more robustness against partial occlusions and illumination changes. In this paper, we (1) extend the AWN method to a view-based approach, ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
The Active Wavelet Network (AWN) [9] approach was recently proposed for automatic face alignment, showing advantages over Active Appearance Models (AAM), such as more robustness against partial occlusions and illumination changes. In this paper, we (1) extend the AWN method to a view-based approach

Coarse-to-fine autoencoder networks (CFAN) for real-time face alignment

by Jie Zhang , Shiguang Shan , Meina Kan , Xilin Chen - In European Conference of Computer Vision , 2014
"... Abstract. Accurate face alignment is a vital prerequisite step for most face perception tasks such as face recognition, facial expression analysis and non-realistic face re-rendering. It can be formulated as the nonlinear inference of the facial landmarks from the detected face region. Deep network ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
Abstract. Accurate face alignment is a vital prerequisite step for most face perception tasks such as face recognition, facial expression analysis and non-realistic face re-rendering. It can be formulated as the nonlinear inference of the facial landmarks from the detected face region. Deep

Modelling Spatio-Temporal Trajectories and Face Signatures on Partially Recurrent Neural Networks

by Alexandra Psarrou, Shaogang Gong, Hilary Buxton
"... We address the problem of trajectory prediction in machine vision applications using variants of Elman’s partially recurrent networks. We use dynamic context to constrain the representation learnt by a network and explore the characteristics of various input representations. Network stability and ge ..."
Abstract - Cited by 15 (6 self) - Add to MetaCart
We address the problem of trajectory prediction in machine vision applications using variants of Elman’s partially recurrent networks. We use dynamic context to constrain the representation learnt by a network and explore the characteristics of various input representations. Network stability

Deep visual-semantic alignments for generating image descriptions

by Andrej Karpathy, Li Fei-fei , 2014
"... We present a model that generates natural language de-scriptions of images and their regions. Our approach lever-ages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between lan-guage and visual data. Our alignment model is based on a novel combinati ..."
Abstract - Cited by 47 (0 self) - Add to MetaCart
combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred

Sequence transduction with recurrent neural networks

by Alex Graves - in ICML 29 , 2012
"... Many machine learning tasks can be ex-pressed as the transformation—or transduc-tion—of input sequences into output se-quences: speech recognition, machine trans-lation, protein secondary structure predic-tion and text-to-speech to name but a few. One of the key challenges in sequence trans-duction ..."
Abstract - Cited by 18 (3 self) - Add to MetaCart
-duction is learning to represent both the in-put and output sequences in a way that is invariant to sequential distortions such as shrinking, stretching and translating. Recur-rent neural networks (RNNs) are a power-ful sequence learning architecture that has proven capable of learning such representa-tions. However

Learning to Align from Scratch

by Gary B. Huang, Marwan A. Mattar, Honglak Lee, Erik Learned-miller
"... Unsupervised joint alignment of images has been demonstrated to improve performance on recognition tasks such as face verification. Such alignment reduces undesired variability due to factors such as pose, while only requiring weak supervision in the form of poorly aligned examples. However, prior w ..."
Abstract - Cited by 23 (4 self) - Add to MetaCart
Unsupervised joint alignment of images has been demonstrated to improve performance on recognition tasks such as face verification. Such alignment reduces undesired variability due to factors such as pose, while only requiring weak supervision in the form of poorly aligned examples. However, prior

Natural Language Processing with Modular PDP Networks and Distributed Lexicon

by Risto Miikkulainen, Michael G. Dyer - Cognitive Science , 1991
"... An approach to connectionist natural language processing is proposed, which is based on hierarchically organized modular Parallel Distributed Processing (PDP) networks and a central lexicon of distributed input/output representations. The modules communicate using these representations, which are gl ..."
Abstract - Cited by 96 (14 self) - Add to MetaCart
are global and publicly available in the system. The representations are developed automatically by all networks while they are learning their processing tasks. The resulting representations reflect the regularities in the subtasks, which facilitates robust processing in the face of noise and damage
Results 11 - 20 of 320