The Experts below are selected from a list of 25977 Experts worldwide ranked by ideXlab platform

Yao Wang - One of the best experts on this subject based on the ideXlab platform.

  • Fingerprint Recognition using translation invariant scattering network
    IEEE Signal Processing in Medicine and Biology Symposium, 2015
    Co-Authors: Shervin Minaee, Yao Wang
    Abstract:

    Fingerprint Recognition has drawn a lot of attention during the last few decades. Different features and algorithms have been used for Fingerprint Recognition in the past. In this paper, a powerful image representation called scattering transform/ network is used for Recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to SIFT descriptors and the higher layers capture higher frequency content of the signal. After extracting the scattering features, their dimensionality is reduced by applying principal component analysis (PCA). In the end, multi-class SVM is used to perform template matching for the Recognition task. The proposed algorithm in this paper is one of the first works which explores the application of deep architecture for Fingerprint Recognition. The proposed scheme is tested on a well-known Fingerprint database and has shown promising results with the best accuracy rate of 98%.

  • Fingerprint Recognition using translation invariant scattering network
    arXiv: Computer Vision and Pattern Recognition, 2015
    Co-Authors: Shervin Minaee, Yao Wang
    Abstract:

    Fingerprint Recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for Fingerprint Recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for Recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture higher frequency content of the signal. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). At the end, multi-class SVM is used to perform template matching for the Recognition task. The proposed scheme is tested on a well-known Fingerprint database and has shown promising results with the best accuracy rate of 98\%.

Jukka Saarinen - One of the best experts on this subject based on the ideXlab platform.

  • Fingerprint Recognition using wavelet features
    ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001
    Co-Authors: Marius Tico, Pauli Kuosmanen, E. Immonen, P. Ramo, Jukka Saarinen
    Abstract:

    A new method of Fingerprint Recognition based on features extracted from the wavelet transform of the discrete image is introduced. The wavelet features are extracted directly from the gray-scale Fingerprint image with no pre-processing (i.e. image enhancement, directional filtering, ridge segmentation, ridge thinning and minutiae extraction). The proposed method has been tested on a small Fingerprint database using the k-nearest neighbor (k-NN) classifier. The very high Recognition rates achieved show that the proposed method may constitutes an efficient solution for a small-scale Fingerprint Recognition system.

  • wavelet domain features for Fingerprint Recognition
    Electronics Letters, 2001
    Co-Authors: Marius Tico, Pauli Kuosmanen, Jukka Saarinen
    Abstract:

    A Fingerprint Recognition approach based on features extracted from the wavelet transform of the discrete image is presented. The efficiency of the method is proven by the high Recognition rates achieved using the k-nearest neighbour (k-NN) classifier. The method requires lower computational effort than most of the Fingerprint Recognition methods proposed to date.

Shervin Minaee - One of the best experts on this subject based on the ideXlab platform.

  • FingerNet: Pushing The Limits of Fingerprint Recognition Using Convolutional Neural Network.
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Shervin Minaee, Elham Azimi, Amirali Abdolrashidi
    Abstract:

    Fingerprint Recognition has been utilized for cellphone authentication, airport security and beyond. Many different features and algorithms have been proposed to improve Fingerprint Recognition. In this paper, we propose an end-to-end deep learning framework for Fingerprint Recognition using convolutional neural networks (CNNs) which can jointly learn the feature representation and perform Recognition. We train our model on a large-scale Fingerprint Recognition dataset, and improve over previous approaches in terms of accuracy. Our proposed model is able to achieve a very high Recognition accuracy on a well-known Fingerprint dataset. We believe this framework can be widely used for biometrics Recognition tasks, making more scalable and accurate systems possible. We have also used a visualization technique to highlight the important areas in an input Fingerprint image, that mostly impact the Recognition results.

  • Fingerprint Recognition using translation invariant scattering network
    IEEE Signal Processing in Medicine and Biology Symposium, 2015
    Co-Authors: Shervin Minaee, Yao Wang
    Abstract:

    Fingerprint Recognition has drawn a lot of attention during the last few decades. Different features and algorithms have been used for Fingerprint Recognition in the past. In this paper, a powerful image representation called scattering transform/ network is used for Recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to SIFT descriptors and the higher layers capture higher frequency content of the signal. After extracting the scattering features, their dimensionality is reduced by applying principal component analysis (PCA). In the end, multi-class SVM is used to perform template matching for the Recognition task. The proposed algorithm in this paper is one of the first works which explores the application of deep architecture for Fingerprint Recognition. The proposed scheme is tested on a well-known Fingerprint database and has shown promising results with the best accuracy rate of 98%.

  • Fingerprint Recognition using translation invariant scattering network
    arXiv: Computer Vision and Pattern Recognition, 2015
    Co-Authors: Shervin Minaee, Yao Wang
    Abstract:

    Fingerprint Recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for Fingerprint Recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for Recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture higher frequency content of the signal. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). At the end, multi-class SVM is used to perform template matching for the Recognition task. The proposed scheme is tested on a well-known Fingerprint database and has shown promising results with the best accuracy rate of 98\%.

Marius Tico - One of the best experts on this subject based on the ideXlab platform.

  • Fingerprint Recognition using wavelet features
    ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001
    Co-Authors: Marius Tico, Pauli Kuosmanen, E. Immonen, P. Ramo, Jukka Saarinen
    Abstract:

    A new method of Fingerprint Recognition based on features extracted from the wavelet transform of the discrete image is introduced. The wavelet features are extracted directly from the gray-scale Fingerprint image with no pre-processing (i.e. image enhancement, directional filtering, ridge segmentation, ridge thinning and minutiae extraction). The proposed method has been tested on a small Fingerprint database using the k-nearest neighbor (k-NN) classifier. The very high Recognition rates achieved show that the proposed method may constitutes an efficient solution for a small-scale Fingerprint Recognition system.

  • wavelet domain features for Fingerprint Recognition
    Electronics Letters, 2001
    Co-Authors: Marius Tico, Pauli Kuosmanen, Jukka Saarinen
    Abstract:

    A Fingerprint Recognition approach based on features extracted from the wavelet transform of the discrete image is presented. The efficiency of the method is proven by the high Recognition rates achieved using the k-nearest neighbour (k-NN) classifier. The method requires lower computational effort than most of the Fingerprint Recognition methods proposed to date.

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

  • The study about Fingerprint Recognition based on orthogonal transforms
    2010 International Conference on Machine Learning and Cybernetics, 2010
    Co-Authors: Li Yang
    Abstract:

    The paper discussed three important orthogonal transforms: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Walsh-Hadamard Transform (DWT). They are important tools for Fingerprint Recognition that is a secure biometric identification Recognition and authentication technology. Their advantages and disadvantages are analyzed and suggested, which will be of great help for Fingerprint Recognition and other digital image process.

  • ICMLC - The study about Fingerprint Recognition based on orthogonal transforms
    2010 International Conference on Machine Learning and Cybernetics, 2010
    Co-Authors: Li Yang
    Abstract:

    The paper discussed three important orthogonal transforms: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Walsh-Hadamard Transform (DWT). They are important tools for Fingerprint Recognition that is a secure biometric identification Recognition and authentication technology. Their advantages and disadvantages are analyzed and suggested, which will be of great help for Fingerprint Recognition and other digital image process.