Histograms of fuzzy oriented gradients for face recognition

Salhi, A. I.; Kardouchi, Mustapha; Belacel, Nabil

doi:10.1109/iccat.2013.6522006

Cited by 18 publications

(12 citation statements)

References 9 publications

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“…To counter these difficulties, logical scoring of preference is used with fuzzy numbers [23]. These are used to be able to classify whether the images are matches or not despite the inexactness of the comparison [24]. Afterward, with an artificial neural network, relevant information from the new scan is stored in the database in order to have more reference for a more accurate reading in future scans.…”

Section: Data and Resultsmentioning

confidence: 99%

Application of Computer Systems in Facial Recognition Efficiency

Africa¹

2019

IJATCSE

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This paper focuses on the application of computer systems in facial recognition efficiency for smartphones. The process of facial recognition is broken down into different stages and a simple system for each step is overviewed. These systems make use of various techniques such as spatial imaging, artificial neural networks, rough set theory, etc. However, as the systems created for this paper are simply overviewed, each stage is not tackled as in-depth. The shortcomings and difficulties encountered in facial recognition are also discussed. The systems created may still be effectively used as a basis for the improvement of existing and the creation of new facial recognition systems, especially if the given systems were to be made more concrete and each stage of image processing were to be tackled and dissected one by one. This paper also briefly discusses other applications of facial recognition outside of smartphones, such as in surveillance.

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Section: Data and Resultsmentioning

confidence: 99%

Application of Computer Systems in Facial Recognition Efficiency

Africa¹

2019

IJATCSE

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“…1, where images or videos are represented as feature vectors. A large number of homologous but different local feature extraction techniques have been proposed with various levels of success in action recognition [2][3][4][5]10]. These techniques utilised the…”

Section: Backbroundmentioning

confidence: 99%

Histogram of Fuzzy Local Spatio-Temporal Descriptors for Video Action Recognition

Zuo

Yang

Liu

et al. 2020

IEEE Trans. Ind. Inf.

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Feature extraction plays a vital role in visual action recognition. Many existing gradient-based feature extractors, including histogram of oriented gradients (HOG), histogram of optical flow (HOF), motion boundary histograms (MBH), and histogram of motion gradients (HMG), build histograms for representing different actions over the spatio-temporal domain in a video. However, these methods require to set the number of bins for information aggregation in advance. Varying numbers of bins usually lead to inherent uncertainty within the process of pixel voting with regard to the bins in the histogram. This paper proposes a novel method to handle such uncertainty by fuzzifying these feature extractors. The proposed approach has two advantages: i) it better represents the ambiguous boundaries between the bins and thus the fuzziness of the spatio-temporal visual information entailed in videos, and ii) the contribution of each pixel is flexibly controlled by a fuzziness parameter for various scenarios. The proposed family of fuzzy descriptors and a combination of them were evaluated on two publicly available datasets, demonstrating that the proposed approach outperforms the original counterparts and other state-of-the-art methods.

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“…The HFOG [9] should reflect a more objective description of content than the conventional HOG features. The main steps stays same as HOG in F-HOG.…”

Section: Histogram Of Fuzzy Oriented Gradient (Hfog)mentioning

confidence: 99%

“…But it is limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG) [9]. There can also increase the performance of LBP [10] by the concept of fuzzy applied to it, called Fuzzy Local Binary Pattern (FLBP).…”

Section: Introductionmentioning

confidence: 99%

CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns

Varghese

2017

IJ-AI

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Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

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Histograms of fuzzy oriented gradients for face recognition

Cited by 18 publications

References 9 publications

Application of Computer Systems in Facial Recognition Efficiency

Application of Computer Systems in Facial Recognition Efficiency

Histogram of Fuzzy Local Spatio-Temporal Descriptors for Video Action Recognition

CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns

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