Local directional pattern of phase congruency features for illumination invariant face recognition

Essa, Almabrok; Asari, Vijayan K.

doi:10.1117/12.2050835

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…Feature extraction is used to acquire the features that are present in the acquired brain signals. Traditional machine learning approaches utilize handmade features by applying several feature extraction algorithms including Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Autoregression, Mean, Median, Standard Deviation (SD), Discrete Cosine Transform (DCT), as well as other techniques that have been have been used and proven highe performance in other application such as local edge/corner feature integration (LFI) [25], local boosted features (LBF) [26], Eigenvectors , and Discrete Wavelet Transform (DWT) [27]. Also, there are some methods that combine two or more different types of techniques as in [28][29][30][31][32].…”

Section: Feature Extractionmentioning

confidence: 99%

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Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

Essa¹,

Kotte²

2022

Preprint

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Brain signals constitute the information that are processed by millions of brain neurons (nerve cells and brain cells). These brain signals can be recorded and analyzed using various of non-invasive techniques such as the Electroencephalograph (EEG), Magneto-encephalograph (MEG) as well as brain-imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and others, which will be discussed briefly in this paper. This paper discusses about the currently emerging techniques such as the usage of different Deep Learning (DL) algorithms for the analysis of these brain signals and how these algorithms will be helpful in determining the neurological status of a person by applying the signal decoding strategy.

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Section: Feature Extractionmentioning

confidence: 99%

“…These methods together resulted in the accurate detection of the epilepsy (a neurological disorder). Similarly, more other feature extraction techniques could be used that combine two or more different types of techniques like [3,4], which could be a potential fed to machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

Essa¹,

Kotte²

2022

Preprint

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“…The key of each face recognition system is the feature extractors, which should be distinct and stable under different conditions. FR system can generally be categorized into one of the two main scenarios based on the characteristics of the images to be matched, such as stillimage-based (still-to-still) FR [2][3][4] or video-based (videoto-video) FR. Also it could be a video-to-still-image-based face recognition system [5].…”

Section: Introductionmentioning

confidence: 99%

High Order Volumetric Directional Pattern for Video‐Based Face Recognition

Essa

Asari

2019

Mathematical Problems in Engineering

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Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches.

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“…Most of the recently proposed approaches belong to this category. Such as local binary pattern (LBP) [16], local ternary pattern (LTP) [17], local directional pattern (LDP) [18], enhanced LDP (EnLDP) [19], local directional number pattern (LDN) [20], directional pattern of phase congruency [21], logarithmic fractal dimension (LFD) [22], Weberface (WF) [23], gradientface (GF) [24] and its modified version orthogonal gradient phase faces (OGPF) [25]. For example, LDP, EnLDP and LDN use Kirsch masks to obtain eight directional edge images and encode the directional information to achieve illumination invariant features.…”

Section: Introductionmentioning

confidence: 99%

Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination

Huixian

Yong-yong

2016

IET Intelligent Transport Systems

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To overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP), which is proved robust to illumination variation, is proposed in this study. First, the Tetrolet transform is performed on the images to obtain low frequency and high frequency components and the retina model processing is applied to low frequency component to make the image more robust to illumination, in the meantime, the authors multiply each element in high frequency components with a scale factor to accentuate details. Then the proposed AWOGBP is used to get the feature vectors of each direction and all the feature vectors are concatenated into the general feature vector for face recognition with the weights of the sub-graph based on their information entropy which is defined as the contribution to describe the whole face images. Finally the principle component analysis method is used to reduce dimensions and the nearest neighbour classifier is used for face image classification and recognition. Experimental results on CMU PIE and Extended Yale B face databases indicate that the proposed method is significantly better as compared with related state-of-the-art methods.

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Local directional pattern of phase congruency features for illumination invariant face recognition

Cited by 6 publications

References 5 publications

Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals

High Order Volumetric Directional Pattern for Video‐Based Face Recognition

Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination

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