The Experts below are selected from a list of 15798 Experts worldwide ranked by ideXlab platform

Xiaoou Tang - One of the best experts on this subject based on the ideXlab platform.

  • learning deep representation for face alignment with auxiliary attributes
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
    Co-Authors: Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang
    Abstract:

    In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proPosed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and Pose Variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.

  • from facial parts responses to face detection a deep learning approach
    arXiv: Computer Vision and Pattern Recognition, 2015
    Co-Authors: Shuo Yang, Ping Luo, Chen Change Loy, Xiaoou Tang
    Abstract:

    In this paper, we proPose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained Pose Variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.

  • facial landmark detection by deep multi task learning
    European Conference on Computer Vision, 2014
    Co-Authors: Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang
    Abstract:

    Facial landmark detection has long been impeded by the problems of occlusion and Pose Variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head Pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proPosed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and Pose Variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [21].

David J. Kriegman - One of the best experts on this subject based on the ideXlab platform.

  • CVPR (1) - Visual tracking using learned linear subspaces
    Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004. CVPR 2004., 2004
    Co-Authors: Jeffrey Ho, Minghsuan Yang, David J. Kriegman
    Abstract:

    This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space. The tracker adaptively updates this subspace while tracking by finding a linear subspace that best approximates the observations made in the previous frames. Instead of the traditional L/sup 2/-reconstruction error norm which leads to subspace estimation using PCA or SVD, we argue that a variant of it, the uniform L/sup 2/-reconstruction error norm, is the right one for tracking. Under this framework we provide a simple and a computationally inexpensive algorithm for finding a subspace whose uniform L/sup 2/-reconstruction error norm for a given collection of data samples is below some threshold, and a simple tracking algorithm is an immediate consequence. We show experimental results on a variety of image sequences of people and man-made objects moving under challenging imaging conditions, which include drastic illumination Variation, partial occlusion and extreme Pose Variation.

Yeehong Yang - One of the best experts on this subject based on the ideXlab platform.

  • face recognition approach based on rank correlation of gabor filtered images
    Pattern Recognition, 2002
    Co-Authors: Olugbenga Ayinde, Yeehong Yang
    Abstract:

    Face recognition is challenging because Variations can be introduced to the pattern of a face by varying Pose, lighting, scale, and expression. A new face recognition approach using rank correlation of Gabor-filtered images is presented. Using this technique, Gabor filters of different sizes and orientations are applied on images before using rank correlation for matching the face representation. The representation used for each face is computed from the Gabor-filtered images and the original image. Although training requires a fairly substantial length of time, the computation time required for recognition is very short. Recognition rates ranging between 83.5% and 96% are obtained using the AT&T (formerly ORL) database using different permutations of 5 and 9 training images per subject. In addition, the effect of Pose Variation on the recognition system is systematically determined using images from the UMIST database.

Matthew Antone - One of the best experts on this subject based on the ideXlab platform.

  • translation templates for object matching across predictable Pose Variation
    British Machine Vision Conference, 2006
    Co-Authors: Chris Stauffer, Matthew Antone
    Abstract:

    Computer vision is the most promising technology for automated, passive tracking of multiple objects over large areas. Effecti ve inter-camera and intra-camera visual tracking can enable information about a vehicle or a pedestrian to be integrated from various sources. Unfortunately, tracking objects across multiple non-overlapping cameras requires reliable comparison of the objects’ appearance under widely-varying view an gles and resolutions. Fortunately, in most cases, an object of a particul ar type entering a scene at a particular position and direction will tend to be i n a very similar Pose. This paper introduces Translation Templates (TTs). TTs exploit this regularity to learn a color-based matching metric for images from a pair of tracking source and sink points, without prior knowledge of object type or object Pose. This model benefits from histogram-based aggre gation while still preserving spatial relationships between the two ima ges. The model can be learned directly from data and used to compare arbitrary types of objects observed from extremely different viewpoints, as long as the relationship between the viewpoints is preserved. This paper describes TTs, describes a method for efficient computation and for visualization of TT s, and presents experimental results from both an indoor pedestrian data set and an outdoor vehicle data set.

  • BMVC - Translation Templates for Object Matching Across Predictable Pose Variation.
    Procedings of the British Machine Vision Conference 2006, 2006
    Co-Authors: Chris Stauffer, Matthew Antone
    Abstract:

    Computer vision is the most promising technology for automated, passive tracking of multiple objects over large areas. Effecti ve inter-camera and intra-camera visual tracking can enable information about a vehicle or a pedestrian to be integrated from various sources. Unfortunately, tracking objects across multiple non-overlapping cameras requires reliable comparison of the objects’ appearance under widely-varying view an gles and resolutions. Fortunately, in most cases, an object of a particul ar type entering a scene at a particular position and direction will tend to be i n a very similar Pose. This paper introduces Translation Templates (TTs). TTs exploit this regularity to learn a color-based matching metric for images from a pair of tracking source and sink points, without prior knowledge of object type or object Pose. This model benefits from histogram-based aggre gation while still preserving spatial relationships between the two ima ges. The model can be learned directly from data and used to compare arbitrary types of objects observed from extremely different viewpoints, as long as the relationship between the viewpoints is preserved. This paper describes TTs, describes a method for efficient computation and for visualization of TT s, and presents experimental results from both an indoor pedestrian data set and an outdoor vehicle data set.

Javier Gonzalez-jimenez - One of the best experts on this subject based on the ideXlab platform.

  • Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots
    Sensors, 2021
    Co-Authors: Samuel-felipe Baltanas, Jose-raul Ruiz-sarmiento, Javier Gonzalez-jimenez
    Abstract:

    Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head Pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head Pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of Poses. Finally, we proPose an optimization method to mitigate said negative effects by considering key Pose samples in the recognition system’s set of known faces. The conducted experiments demonstrate that this optimized set of Poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace.