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    Wyświetlanie 1-4 z 4
    Tytuł:
    Hand-drawn face sketch recognition using rank-level fusion of image quality assessment metrics
    Autorzy:
    Mahfoud, Sami
    Daamouche, Abdelhamid
    Bengherabi, Messaoud
    Hadid, Abdenour
    Powiązania:
    https://bibliotekanauki.pl/articles/2173718.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    face sketch recognition
    synthesized face sketch
    rank-level fusion
    IQA metrics
    rozpoznawanie szkiców twarzy
    zsyntetyzowany szkic twarzy
    fuzja na poziomie rangi
    metryka IQA
    Opis:
    Face Sketch Recognition (FSR) presents a severe challenge to conventional recognition paradigms developed basically to match face photos. This challenge is mainly due to the large texture discrepancy between face sketches, characterized by shape exaggeration, and face photos. In this paper, we propose a training-free synthesized face sketch recognition method based on the rank-level fusion of multiple Image Quality Assessment (IQA) metrics. The advantages of IQA metrics as a recognition engine are combined with the rank-level fusion to boost the final recognition accuracy. By integrating multiple IQA metrics into the face sketch recognition framework, the proposed method simultaneously performs face-sketch matching application and evaluates the performance of face sketch synthesis methods. To test the performance of the recognition framework, five synthesized face sketch methods are used to generate sketches from face photos. We use the Borda count approach to fuse four IQA metrics, namely, structured similarity index metric, feature similarity index metric, visual information fidelity and gradient magnitude similarity deviation at the rank-level. Experimental results and comparison with the state-of-the-art methods illustrate the competitiveness of the proposed synthesized face sketch recognition framework.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 6; art. no. e143554
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Super-resolution reconstruction of face images based on pre-amplification non-negative restricted neighborhood embedding
    Autorzy:
    Yang, X.
    Liu, D.
    Zhou, D.
    Fei, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/201163.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    super-resolution
    neighborhood embedding
    nonnegative restriction
    face reconstruction
    superrozdzielczość
    rozpoznawanie twarzy
    rekonstrukcja twarzy
    Opis:
    The traditional super-resolution (SR) reconstruction algorithm based on neighborhood embedding preserves the local geometric structure of image block manifold to reconstruct high-resolution (HR) manifold. However, when the magnification is large, the low resolution (LR) image is seriously degraded and most of the information is lost after down-sampling. The neighborhood relation of the LR manifold can not reflect the inherent data structure. In order to solve the problem effectively, we propose a face image SR algorithm based on pre-amplification non-negative restricted neighborhood embedding. In the training phase, the LR image is pre-amplified so that there are more similar manifold structures between the HR and LR resolution images. The constraints of the reconstructed coefficients are loosened and the HR image blocks are iteratively updated to obtain the reconstructed weights. The experimental results show that the proposed method has a better reconstruction effect compared with some traditional learning algorithms.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 899-905
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multimodal face recognition method with two-dimensional hidden Markov model
    Autorzy:
    Bobulski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/201711.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    pattern recognition
    biometrics
    3D face recognition
    hidden Markov model
    rozpoznawanie wzorców
    biometria
    rozpoznawanie twarzy 3D
    ukryty model Markowa
    Opis:
    The paper presents a new solution for the face recognition based on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D and 3D image processing, because part of the information is lost during the conversion to one-dimensional features vector. The paper presents a concept of the full ergodic 2DHMM, which can be used in 2D and 3D face recognition. The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimodal (2D+3D) face images recognition, and is faster than ICP method.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2017, 65, 1; 121-128
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition
    Autorzy:
    Szmurło, Robert
    Osowski, Stanisław
    Powiązania:
    https://bibliotekanauki.pl/articles/2173680.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    CNN
    ensemble of classifiers
    face recognition
    feature selection
    convolutional neural networks
    splotowe sieci neuronowe
    zespół klasyfikatorów
    rozpoznawanie twarzy
    wybór funkcji
    Opis:
    The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141004
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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