Variant Pose Face Recognition Using Discrete Wavelet Transform and Linear Regression

Shahdi, Seyed Omid; Abu-Bakar, S. A. R.

doi:10.1142/s0218001412560137

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…26 Global approaches use an entire image of face to estimate head pose. 33 The principal advantage of the approaches is that only the face needs to be located. Osadchy et al 28 instead use a convolutional network to learn the mapping for head pose estimation and can achieve real-time performance for the problem.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Regression Approach for Unconstrained Face Analysis

Liu

Chen

Shan

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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Head pose and facial feature detection are important for face analysis. However, many studies reported good results in constrained environment, the performance could be decreased due to the high variations in facial appearance, poses, illumination, occlusion, expression and make-up. In this paper, we propose a hierarchical regression approach, Dirichlet-tree enhanced random forests (D-RF) for face analysis in unconstrained environment. D-RF introduces Dirichlet-tree probabilistic model into regression RF framework in the hierarchical way to achieve the e±ciency and robustness. To eliminate noise in°uence of unconstrained environment, facial patches extracted from face area are classi¯ed as positive or negative facial patches, only positive facial patches are used for face analysis. The proposed hierarchical D-RF works in two iterative procedures. First, coarse head pose is estimated to constrain the facial features detection, then the head pose is updated based on the estimated facial features. Second, the facial feature localization is re¯ned based on the updated head pose. In order to further improve the e±ciency and robustness, multiple probabilitic models are learned in leaves of the D-RF, i.e. the patch's classi¯cation, the head pose probabilities, the locations of facial points and face deformation models (FDM). Moreover, our algorithm takes a composite weight voting method, where each patch extracted from the image can directly cast a vote for the head pose or each of the facial features. Extensive experiments have been done with di®erent publicly available databases. The experimental results demonstrate that the proposed approach is robust and e±cient for head pose and facial feature detection.

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Section: Related Workmentioning

confidence: 99%

A Hierarchical Regression Approach for Unconstrained Face Analysis

Liu

Chen

Shan

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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“…It includes Gabor Filter (GF), Local Binary Pattern (LBP), Histograms of Oriented Gradients (HOG), and etc. [6,[16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Esmaeili,

Mohassel Feghhi,

Shahdi

2024

Scientia Iranica

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The contagious and pandemic COVID-19 disease is currently considered as the main health concern and posed widespread panic across human-beings. It affects the human respiratory tract and lungs intensely. So that it has imposed significant threats for premature death. Although, its early diagnosis can play a vital role in revival phase, the radiography tests with the manual intervention are a time-consuming process. Time is also limited for such manual inspecting of numerous patients in the hospitals. Thus, the necessity of automatic diagnosis on the chest X-ray or the CT images with a high efficient performance is urgent. Toward this end, we propose a novel method, named as the ULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. In fact, this method includes Uniform Local Binary Pattern (ULBP), Gabor Filter (GF), and ResNet51. According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy.

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Neural Network-Based Approach for Face Recognition Across Varying Pose

Shahdi

Abu-Bakar

2015

Int. J. Patt. Recogn. Artif. Intell.

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At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

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Variant Pose Face Recognition Using Discrete Wavelet Transform and Linear Regression

Cited by 3 publications

References 27 publications

A Hierarchical Regression Approach for Unconstrained Face Analysis

A Hierarchical Regression Approach for Unconstrained Face Analysis

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Neural Network-Based Approach for Face Recognition Across Varying Pose

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