A new Global-Gabor-Zernike feature descriptor and its application to face recognition
•This article combines structural and statistical features for face recognition.•Statistical features are introduced by applying the Zernike moments on Gabor filters.•The HOG descriptor is used to extract local statistical features.•The proposed method outperforms other methods on ORL, Yale, AR and...
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| Published in | Journal of visual communication and image representation Vol. 38; pp. 65 - 72 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.07.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1047-3203 1095-9076 |
| DOI | 10.1016/j.jvcir.2016.02.010 |
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| Abstract | •This article combines structural and statistical features for face recognition.•Statistical features are introduced by applying the Zernike moments on Gabor filters.•The HOG descriptor is used to extract local statistical features.•The proposed method outperforms other methods on ORL, Yale, AR and LFW datasets.
Face recognition is an important subject in computer vision and authentication systems. Feature extraction is one of the main steps in the face recognition systems, which greatly affects recognition accuracy. In the most of the existing methods, only local features in the facial area are extracted and employed in recognizing the person’s face. In this article, at first a novel multi-scale and rotation invariant global feature descriptor is introduced by applying the Zernike moment on the outputs of Gabor filters. Then the proposed global feature along with an efficient local feature, the histogram of oriented gradient (HOG), is employed to propose a new face recognition system. The proposed system was tested on three famous face recognition databases, namely ORL, Yale and AR and face recognition rates of 98%, 97.8% and 97.1% were obtained respectively. These rates are higher than other state-of-the-art methods. |
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| AbstractList | Face recognition is an important subject in computer vision and authentication systems. Feature extraction is one of the main steps in the face recognition systems, which greatly affects recognition accuracy. In the most of the existing methods, only local features in the facial area are extracted and employed in recognizing the person's face. In this article, at first a novel multi-scale and rotation invariant global feature descriptor is introduced by applying the Zernike moment on the outputs of Gabor filters. Then the proposed global feature along with an efficient local feature, the histogram of oriented gradient (HOG), is employed to propose a new face recognition system. The proposed system was tested on three famous face recognition databases, namely ORL, Yale and AR and face recognition rates of 98%, 97.8% and 97.1% were obtained respectively. These rates are higher than other state-of-the-art methods. •This article combines structural and statistical features for face recognition.•Statistical features are introduced by applying the Zernike moments on Gabor filters.•The HOG descriptor is used to extract local statistical features.•The proposed method outperforms other methods on ORL, Yale, AR and LFW datasets. Face recognition is an important subject in computer vision and authentication systems. Feature extraction is one of the main steps in the face recognition systems, which greatly affects recognition accuracy. In the most of the existing methods, only local features in the facial area are extracted and employed in recognizing the person’s face. In this article, at first a novel multi-scale and rotation invariant global feature descriptor is introduced by applying the Zernike moment on the outputs of Gabor filters. Then the proposed global feature along with an efficient local feature, the histogram of oriented gradient (HOG), is employed to propose a new face recognition system. The proposed system was tested on three famous face recognition databases, namely ORL, Yale and AR and face recognition rates of 98%, 97.8% and 97.1% were obtained respectively. These rates are higher than other state-of-the-art methods. |
| Author | Alirezazadeh, Pendar Fathi, Abdolhossein Abdali-Mohammadi, Fardin |
| Author_xml | – sequence: 1 givenname: Abdolhossein surname: Fathi fullname: Fathi, Abdolhossein email: a.fathi@razi.ac.ir – sequence: 2 givenname: Pendar surname: Alirezazadeh fullname: Alirezazadeh, Pendar email: a.pendar@pgs.razi.ac.ir – sequence: 3 givenname: Fardin surname: Abdali-Mohammadi fullname: Abdali-Mohammadi, Fardin email: fardin.abdali@razi.ac.ir |
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| Keywords | Global feature descriptor Face recognition Global gabor zernike descriptor Zernike moment Histogram of oriented gradients (HOG) Feature extraction Local feature descriptor Gabor wavelet |
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| Snippet | •This article combines structural and statistical features for face recognition.•Statistical features are introduced by applying the Zernike moments on Gabor... Face recognition is an important subject in computer vision and authentication systems. Feature extraction is one of the main steps in the face recognition... |
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| SubjectTerms | Augmented reality Authentication Face recognition Feature extraction Feature recognition Gabor wavelet Global feature descriptor Global gabor zernike descriptor Histogram of oriented gradients (HOG) Invariants Local feature descriptor Representations Visual Zernike moment |
| Title | A new Global-Gabor-Zernike feature descriptor and its application to face recognition |
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