The Experts below are selected from a list of 42498 Experts worldwide ranked by ideXlab platform

Yalda Mohsenzadeh - One of the best experts on this subject based on the ideXlab platform.

  • low resolution face recognition using a two branch deep convolutional neural network architecture
    Expert Systems With Applications, 2020
    Co-Authors: Erfan Zangeneh, Mohammad Rahmati, Yalda Mohsenzadeh
    Abstract:

    Abstract We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face Images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution Images consists of 14 layers and the other branch which maps the low resolution face Images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution Images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution Probe face Images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution Image from its corresponding low resolution Probe Image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality.

  • low resolution face recognition using a two branch deep convolutional neural network architecture
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Erfan Zangeneh, Mohammad Rahmati, Yalda Mohsenzadeh
    Abstract:

    We propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face Images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution Images consists of 14 layers and the other branch which maps the low resolution face Images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution Images are backpropagated to train the networks. Our proposed method is evaluated on FERET data set and compared with state-of-the-art competing methods. Our extensive experimental results show that the proposed method significantly improves the recognition performance especially for very low resolution Probe face Images (11.4% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution Image from its corresponding low resolution Probe Image which is comparable with state-of-the-art super-resolution methods in terms of visual quality.

Erfan Zangeneh - One of the best experts on this subject based on the ideXlab platform.

  • low resolution face recognition using a two branch deep convolutional neural network architecture
    Expert Systems With Applications, 2020
    Co-Authors: Erfan Zangeneh, Mohammad Rahmati, Yalda Mohsenzadeh
    Abstract:

    Abstract We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face Images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution Images consists of 14 layers and the other branch which maps the low resolution face Images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution Images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution Probe face Images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution Image from its corresponding low resolution Probe Image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality.

  • low resolution face recognition using a two branch deep convolutional neural network architecture
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Erfan Zangeneh, Mohammad Rahmati, Yalda Mohsenzadeh
    Abstract:

    We propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face Images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution Images consists of 14 layers and the other branch which maps the low resolution face Images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution Images are backpropagated to train the networks. Our proposed method is evaluated on FERET data set and compared with state-of-the-art competing methods. Our extensive experimental results show that the proposed method significantly improves the recognition performance especially for very low resolution Probe face Images (11.4% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution Image from its corresponding low resolution Probe Image which is comparable with state-of-the-art super-resolution methods in terms of visual quality.

Mohammed Bennamoun - One of the best experts on this subject based on the ideXlab platform.

  • robust regression for face recognition
    Pattern Recognition, 2012
    Co-Authors: Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    Abstract:

    In this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace (Barsi and Jacobs, 2003 [1]), we develop a linear model representing a Probe Image as a linear combination of class specific galleries. In the presence of noise, the well-conditioned inverse problem is solved using the robust Huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Robust Linear Regression Classification (RLRC) algorithm is extensively evaluated for two important cases of robustness i.e. illumination variations and random pixel corruption. Illumination invariant face recognition is demonstrated on three standard databases under exemplary evaluation protocols reported in the literature. Comprehensive comparative analysis with the state-of-art illumination tolerant approaches indicates a comparable performance index for the proposed RLRC algorithm. The efficiency of the proposed approach in the presence of severe random noise is validated under several exemplary noise models such as dead-pixel problem, salt and pepper noise, speckle noise and Additive White Gaussian Noise (AWGN). The RLRC algorithm is found to be favorable compared with the benchmark generative approaches.

  • linear regression for face recognition
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
    Co-Authors: Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    Abstract:

    In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a Probe Image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.

  • robust regression for face recognition
    International Conference on Pattern Recognition, 2010
    Co-Authors: Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    Abstract:

    In this paper we address the problem of illumination invariant face recognition. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace [2], we develop a linear model representing a Probe Image as a linear combination of class-specific galleries. In the presence of noise, the well-conditioned inverse problem is solved using the robust Huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Robust Linear Regression Classification (RLRC) algorithm is extensively evaluated for two standard databases and has shown good performance index compared to the state-of-art robust approaches.

Mohammad Rahmati - One of the best experts on this subject based on the ideXlab platform.

  • low resolution face recognition using a two branch deep convolutional neural network architecture
    Expert Systems With Applications, 2020
    Co-Authors: Erfan Zangeneh, Mohammad Rahmati, Yalda Mohsenzadeh
    Abstract:

    Abstract We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face Images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution Images consists of 14 layers and the other branch which maps the low resolution face Images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution Images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution Probe face Images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution Image from its corresponding low resolution Probe Image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality.

  • low resolution face recognition using a two branch deep convolutional neural network architecture
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Erfan Zangeneh, Mohammad Rahmati, Yalda Mohsenzadeh
    Abstract:

    We propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face Images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution Images consists of 14 layers and the other branch which maps the low resolution face Images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution Images are backpropagated to train the networks. Our proposed method is evaluated on FERET data set and compared with state-of-the-art competing methods. Our extensive experimental results show that the proposed method significantly improves the recognition performance especially for very low resolution Probe face Images (11.4% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution Image from its corresponding low resolution Probe Image which is comparable with state-of-the-art super-resolution methods in terms of visual quality.

Imran Naseem - One of the best experts on this subject based on the ideXlab platform.

  • robust regression for face recognition
    Pattern Recognition, 2012
    Co-Authors: Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    Abstract:

    In this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace (Barsi and Jacobs, 2003 [1]), we develop a linear model representing a Probe Image as a linear combination of class specific galleries. In the presence of noise, the well-conditioned inverse problem is solved using the robust Huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Robust Linear Regression Classification (RLRC) algorithm is extensively evaluated for two important cases of robustness i.e. illumination variations and random pixel corruption. Illumination invariant face recognition is demonstrated on three standard databases under exemplary evaluation protocols reported in the literature. Comprehensive comparative analysis with the state-of-art illumination tolerant approaches indicates a comparable performance index for the proposed RLRC algorithm. The efficiency of the proposed approach in the presence of severe random noise is validated under several exemplary noise models such as dead-pixel problem, salt and pepper noise, speckle noise and Additive White Gaussian Noise (AWGN). The RLRC algorithm is found to be favorable compared with the benchmark generative approaches.

  • linear regression for face recognition
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
    Co-Authors: Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    Abstract:

    In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a Probe Image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.

  • robust regression for face recognition
    International Conference on Pattern Recognition, 2010
    Co-Authors: Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    Abstract:

    In this paper we address the problem of illumination invariant face recognition. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace [2], we develop a linear model representing a Probe Image as a linear combination of class-specific galleries. In the presence of noise, the well-conditioned inverse problem is solved using the robust Huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Robust Linear Regression Classification (RLRC) algorithm is extensively evaluated for two standard databases and has shown good performance index compared to the state-of-art robust approaches.