The Experts below are selected from a list of 552 Experts worldwide ranked by ideXlab platform

Kap Luk Chan - One of the best experts on this subject based on the ideXlab platform.

  • support vector machines for face recognition
    Image and Vision Computing, 2001
    Co-Authors: Stan Z Li, Kap Luk Chan
    Abstract:

    Abstract Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performance of the SVMs based face recognition is compared with the standard Eigenface Approach, and also the more recently proposed algorithm called the nearest feature line (NFL).

  • face recognition by support vector machines
    IEEE International Conference on Automatic Face and Gesture Recognition, 2000
    Co-Authors: Stan Z Li, Kap Luk Chan
    Abstract:

    Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard Eigenface Approach using the nearest center classification (NCC) criterion.

Rangarajan Lalitha - One of the best experts on this subject based on the ideXlab platform.

  • Mugshot Identification from Manipulated Facial Images
    2012
    Co-Authors: Chennamma H. R., Rangarajan Lalitha
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.Comment: 8 pages, 5 figures, 1 table, journal. arXiv admin note: substantial text overlap with arXiv:1106.490

  • Face Identification from Manipulated Facial Images using SIFT
    2011
    Co-Authors: Chennamma H. R., Rangarajan Lalitha
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.Comment: 4 pages, 4 figures, IEEE 3rd International Conference on Emerging Trends in Engineering & Technology (ICETET'2010), Nov 19-21, 2010, Goa, Indi

L. Pratap Reddy - One of the best experts on this subject based on the ideXlab platform.

  • Performance evaluation of incremental training method for face recognition using PCA
    Journal of Real-Time Image Processing, 2007
    Co-Authors: Ch. Satyanarayana, D. M. Potukuchi, L. Pratap Reddy
    Abstract:

    Relevance of ‘face recognition’ (FR) in the modern world requirements is presented as a case of human machine interaction. Physical conditions that influence the face recognition process regarding the facial features, illumination changes and viewing angles etc. are discussed. Face recognition process predominantly depends on machine perception i.e. information through an array of pixels with respect to the facial image. Details of Eigenface Approach through the involvement of contemporary algebraic and statistical analysis are revisited. Methodology involved in the Principal Component Analysis and advantages of exposing the data to incremental training (using PCA) are discussed. A model for the implementation of IPCA over the face databases is proposed to estimate its performance for the face recognition process. Performance of the present model is studied in the domain of Euclidean distance, decay parameter, recognition rate, eigenvalues and overall computational time. Present IPCA model administered over standard ORL, FERET databases along with that over the JNTU face database with large number of face images revealed relative performance. The merit of present IPCA is inferred through enhanced recognition rate and reduced complexity (in the algorithm), intelligent eigenvectors and lesser computational time. The results are presented in the wake of the body of data available with other methods.

Lalitha Rangarajan - One of the best experts on this subject based on the ideXlab platform.

  • Mugshot Identification from Manipulated Facial Images
    arXiv: Computer Vision and Pattern Recognition, 2012
    Co-Authors: H. R. Chennamma, Lalitha Rangarajan
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.

  • Face Identification from Manipulated Facial Images using SIFT
    arXiv: Computer Vision and Pattern Recognition, 2011
    Co-Authors: H. R. Chennamma, Lalitha Rangarajan, Veerabhadrappa
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.

  • ICETET - Face Identification from Manipulated Facial Images Using SIFT
    2010 3rd International Conference on Emerging Trends in Engineering and Technology, 2010
    Co-Authors: H. R. Chennamma, Lalitha Rangarajan, Veerabhadrappa
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugs hot identification in which mugs hot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.

H. R. Chennamma - One of the best experts on this subject based on the ideXlab platform.

  • Mugshot Identification from Manipulated Facial Images
    arXiv: Computer Vision and Pattern Recognition, 2012
    Co-Authors: H. R. Chennamma, Lalitha Rangarajan
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.

  • Face Identification from Manipulated Facial Images using SIFT
    arXiv: Computer Vision and Pattern Recognition, 2011
    Co-Authors: H. R. Chennamma, Lalitha Rangarajan, Veerabhadrappa
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.

  • ICETET - Face Identification from Manipulated Facial Images Using SIFT
    2010 3rd International Conference on Emerging Trends in Engineering and Technology, 2010
    Co-Authors: H. R. Chennamma, Lalitha Rangarajan, Veerabhadrappa
    Abstract:

    Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugs hot identification in which mugs hot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known Eigenface Approach. Experiments have been conducted with real case images to evaluate the performance of both methods.