Face Representation

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Hongming Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for Face Representation and recognition
    Proceedings of the IEEE International Conference on Computer Vision, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

  • ICCV - Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for Face Representation and recognition
    Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

Wen Gao - One of the best experts on this subject based on the ideXlab platform.

  • Learned local Gabor patterns for Face Representation and recognition
    Signal Processing, 2009
    Co-Authors: Shufu Xie, Xilin Chen, Shiguang Shan, Xin Meng, Wen Gao
    Abstract:

    In this paper, we propose Learned Local Gabor Patterns (LLGP) for Face Representation and recognition. The proposed method is based on Gabor feature and the concept of texton, and defines the feature cliques which appear frequently in Gabor features as the basic patterns. Different from Local Binary Patterns (LBP) whose patterns are predefined, the local patterns in our approach are learned from the patch set, which is constructed by sampling patches from Gabor filtered Face images. Thus, the patterns in our approach are Face-specific and desirable for Face perception tasks. Based on these learned patterns, each facial image is converted into multiple pattern maps and the block-based histograms of these patterns are concatenated together to form the Representation of the Face image. In addition, we propose an effective weighting strategy to enhance the performances, which makes use of the discriminative powers of different facial parts as well as different patterns. The proposed approach is evaluated on two Face databases: FERET and CAS-PEAL-R1. Extensive experimental results and comparisons with existing methods show the effectiveness of the LLGP Representation method and the weighting strategy. Especially, heterogeneous testing results show that the LLGP codebook has very impressive generalizability for unseen data.

  • ICPR - V-LGBP: Volume based local Gabor binary patterns for Face Representation and recognition
    2008 19th International Conference on Pattern Recognition, 2008
    Co-Authors: Shufu Xie, Xilin Chen, Shiguang Shan, Wen Gao
    Abstract:

    In this paper, we propose volume based local Gabor binary patterns (V-LGBP) for Face Representation and recognition. In our method, the Gabor feature set of each gray image is regarded as a three dimensional ldquovolumerdquo, where the first two dimensions are spatial domain and the third dimension is the Gabor filter index. Then, the neighborhood order relationship in the ldquovolumerdquo is encoded by local binary patterns (LBP), which converts the Gabor transformed images into multiple index maps. Finally, the spatial histograms of all the V-LGBP index maps are concatenated together to represent the facial appearances. In addition, in order to reflect the uniform appearances of V-LGBP, its uniform patterns are redefined via statistical analysis. Extensive experiments on FERET dataset validate the effectiveness of our approach.

  • Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for Face Representation and recognition
    Proceedings of the IEEE International Conference on Computer Vision, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

  • ICCV - Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for Face Representation and recognition
    Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

Song-chun Zhu - One of the best experts on this subject based on the ideXlab platform.

  • A Hierarchical Compositional Model for Face Representation and Sketching
    IEEE transactions on pattern analysis and machine intelligence, 2008
    Co-Authors: Hong Chen, Song-chun Zhu, Jiebo Luo
    Abstract:

    This paper presents a hierarchical-compositional model of human Faces, as a three-layer AND-OR graph to account for the structural variabilities over multiple resolutions. In the AND-OR graph, an AND-node represents a decomposition of certain graphical structure, which expands to a set of OR-nodes with associated relations; an OR-node serves as a switch variable pointing to alternative AND-nodes. Faces are then represented hierarchically: The first layer treats each Face as a whole, the second layer refines the local facial parts jointly as a set of individual templates, and the third layer further divides the Face into 15 zones and models detail facial features such as eye corners, marks, or wrinkles. Transitions between the layers are realized by measuring the minimum description length (MDL) given the complexity of an input Face image. Diverse Face Representations are formed by drawing from dictionaries of global Faces, parts, and skin detail features. A sketch captures the most informative part of a Face in a much more concise and potentially robust Representation. However, generating good facial sketches is extremely challenging because of the rich facial details and large structural variations, especially in the high-resolution images. The representing power of our generative model is demonstrated by reconstructing high-resolution Face images and generating the cartoon facial sketches. Our model is useful for a wide variety of applications, including recognition, nonphotorealisitc rendering, superresolution, and low-bit rate Face coding.

  • A High Resolution Grammatical Model for Face Representation and Sketching
    2005
    Co-Authors: Hong Chen, Song-chun Zhu
    Abstract:

    In this paper we present a generative, high resolution Face Representation which extends the well- known active appearance model (AAM)[5], [6], [7] with two additional layers. (i) One layer refines the global AAM (PCA) model with a dictionary of learned Face components to account for the shape and intensity variabilities of eyes, eyebrows, nose and mouth. (ii) The other layer divides the Face skin into 9 zones with a learned dictionary of sketch primitives to represent skin marks and wrinkles. This model is no longer of fixed dimensions and is flexible for it can select the diverse Representations in the dictionaries of Face components and skin features depending on the complexity of the Face. The selection is modulated by the grammatical rules through hidden ”switch” variables. Our comparison experiments demonstrate that this model can achieve nearly lossless coding of Face at high resolution (256 × 256 pixels) with low bits. We also show that the generative model can easily generate cartoon sketches by changing the rendering dictionary. Our Face model is aimed at a number of applications including cartoon sketch in non-photorealistic rendering, super-resolution in image processing, and low bit Face communication in wireless platforms.

  • CVPR (2) - A high resolution grammatical model for Face Representation and sketching
    2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 1
    Co-Authors: Hong Chen, Song-chun Zhu
    Abstract:

    In this paper we present a generative, high resolution Face Representation which extends the well-known active appearance model (AAM) with two additional layers. (i) One layer refines the global AAM (PCA) model with a dictionary of learned Face components to account for the shape and intensity variabilities of eyes, eyebrows, nose and mouth, (ii) the other layer divides the Face skin into 9 zones with a learned dictionary of sketch primitives to represent skin marks and wrinkles. This model is no longer of fixed dimensions and is flexible for it can select the diverse Representations in the dictionaries of Face components and skin features depending on the complexity of the Face. The selection is modulated by the grammatical rules through hidden "switch " variables. Our comparison experiments demonstrate that this model can achieve nearly lossless coding of Face at high resolution (256 /spl times/ 256 pixels) with low bits. We also show that the generative model can easily generate cartoon sketches by changing the rendering dictionary. Our Face model is aimed at a number of applications including cartoon sketch in non-photorealistic rendering, super-resolution in image processing, and low bit Face communication in wireless platforms.

Xilin Chen - One of the best experts on this subject based on the ideXlab platform.

  • Learned local Gabor patterns for Face Representation and recognition
    Signal Processing, 2009
    Co-Authors: Shufu Xie, Xilin Chen, Shiguang Shan, Xin Meng, Wen Gao
    Abstract:

    In this paper, we propose Learned Local Gabor Patterns (LLGP) for Face Representation and recognition. The proposed method is based on Gabor feature and the concept of texton, and defines the feature cliques which appear frequently in Gabor features as the basic patterns. Different from Local Binary Patterns (LBP) whose patterns are predefined, the local patterns in our approach are learned from the patch set, which is constructed by sampling patches from Gabor filtered Face images. Thus, the patterns in our approach are Face-specific and desirable for Face perception tasks. Based on these learned patterns, each facial image is converted into multiple pattern maps and the block-based histograms of these patterns are concatenated together to form the Representation of the Face image. In addition, we propose an effective weighting strategy to enhance the performances, which makes use of the discriminative powers of different facial parts as well as different patterns. The proposed approach is evaluated on two Face databases: FERET and CAS-PEAL-R1. Extensive experimental results and comparisons with existing methods show the effectiveness of the LLGP Representation method and the weighting strategy. Especially, heterogeneous testing results show that the LLGP codebook has very impressive generalizability for unseen data.

  • ICPR - V-LGBP: Volume based local Gabor binary patterns for Face Representation and recognition
    2008 19th International Conference on Pattern Recognition, 2008
    Co-Authors: Shufu Xie, Xilin Chen, Shiguang Shan, Wen Gao
    Abstract:

    In this paper, we propose volume based local Gabor binary patterns (V-LGBP) for Face Representation and recognition. In our method, the Gabor feature set of each gray image is regarded as a three dimensional ldquovolumerdquo, where the first two dimensions are spatial domain and the third dimension is the Gabor filter index. Then, the neighborhood order relationship in the ldquovolumerdquo is encoded by local binary patterns (LBP), which converts the Gabor transformed images into multiple index maps. Finally, the spatial histograms of all the V-LGBP index maps are concatenated together to represent the facial appearances. In addition, in order to reflect the uniform appearances of V-LGBP, its uniform patterns are redefined via statistical analysis. Extensive experiments on FERET dataset validate the effectiveness of our approach.

  • Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for Face Representation and recognition
    Proceedings of the IEEE International Conference on Computer Vision, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

  • ICCV - Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for Face Representation and recognition
    Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

Shiguang Shan - One of the best experts on this subject based on the ideXlab platform.

  • Learned local Gabor patterns for Face Representation and recognition
    Signal Processing, 2009
    Co-Authors: Shufu Xie, Xilin Chen, Shiguang Shan, Xin Meng, Wen Gao
    Abstract:

    In this paper, we propose Learned Local Gabor Patterns (LLGP) for Face Representation and recognition. The proposed method is based on Gabor feature and the concept of texton, and defines the feature cliques which appear frequently in Gabor features as the basic patterns. Different from Local Binary Patterns (LBP) whose patterns are predefined, the local patterns in our approach are learned from the patch set, which is constructed by sampling patches from Gabor filtered Face images. Thus, the patterns in our approach are Face-specific and desirable for Face perception tasks. Based on these learned patterns, each facial image is converted into multiple pattern maps and the block-based histograms of these patterns are concatenated together to form the Representation of the Face image. In addition, we propose an effective weighting strategy to enhance the performances, which makes use of the discriminative powers of different facial parts as well as different patterns. The proposed approach is evaluated on two Face databases: FERET and CAS-PEAL-R1. Extensive experimental results and comparisons with existing methods show the effectiveness of the LLGP Representation method and the weighting strategy. Especially, heterogeneous testing results show that the LLGP codebook has very impressive generalizability for unseen data.

  • ICPR - V-LGBP: Volume based local Gabor binary patterns for Face Representation and recognition
    2008 19th International Conference on Pattern Recognition, 2008
    Co-Authors: Shufu Xie, Xilin Chen, Shiguang Shan, Wen Gao
    Abstract:

    In this paper, we propose volume based local Gabor binary patterns (V-LGBP) for Face Representation and recognition. In our method, the Gabor feature set of each gray image is regarded as a three dimensional ldquovolumerdquo, where the first two dimensions are spatial domain and the third dimension is the Gabor filter index. Then, the neighborhood order relationship in the ldquovolumerdquo is encoded by local binary patterns (LBP), which converts the Gabor transformed images into multiple index maps. Finally, the spatial histograms of all the V-LGBP index maps are concatenated together to represent the facial appearances. In addition, in order to reflect the uniform appearances of V-LGBP, its uniform patterns are redefined via statistical analysis. Extensive experiments on FERET dataset validate the effectiveness of our approach.

  • Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for Face Representation and recognition
    Proceedings of the IEEE International Conference on Computer Vision, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.

  • ICCV - Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for Face Representation and recognition
    Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
    Co-Authors: Wenchao Zhang, Xilin Chen, Wen Gao, Shiguang Shan, Hongming Zhang
    Abstract:

    For years, researchers in Face recognition area have been representing and recognizing Faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based Face Representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the Face model, so that the generalizability problem is naturally avoided. In this approach, a Face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET Face database show the validity of the proposed approach especially for partially occluded Face images, and more impressively, we have achieved the best result on FERET Face database.