Face Recognition Based on Adaptive Singular Value Decomposition in the Wavelet Domain

Wang, Jing-Wein; Chen, Tzu-Hsiung

doi:10.1007/978-3-319-58753-0_59

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Many improved algorithms based on PCA or SVD are proposed, such as kernel PCA (KPCA) [33], modular PCA (MPCA) [34], twodimensional PCA (2DPCA) [35,36], weighted subpattern PCA (Aw-SpPCA) [37], discriminative K-SVD (DK-SVD) [38], flustered SVD (FSVD) [39], but their performances are still not very good especially under complex illumination. Therefore, a series of improved algorithms are further proposed by combining with other methods, such as wavelet transform [40,41], correlation filter [42], linear discriminant analysis (LDA) [43], retina modelling [44], virtual representation [45] etc. Moreover, in order to to utilise samples' high-order statistical characteristics, other algorithms are proposed, such as linear discriminant analysis (IDA) [46], discrete cosine transform (DCT) [47] etc.…”

Section: Introductionmentioning

confidence: 99%

A novel face recognition method based on fusion of LBP and HOG

Chen

Gao

et al. 2021

IET Image Processing

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As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by center symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

A novel face recognition method based on fusion of LBP and HOG

Chen

Gao

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

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Recognizing Face Using the Combination of Singular Value Decomposition and Hidden Markov Model Algorithms

Candra

Sulaiman

2023

Lecture Notes in Electrical Engineering

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Face Recognition Attendance System Method Based on Fusion of LBP and HOG

Mahajan

Subramanium

2022

IJARSCT

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As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by centre symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.

show abstract

Face Recognition Based on Adaptive Singular Value Decomposition in the Wavelet Domain

Cited by 3 publications

References 11 publications

A novel face recognition method based on fusion of LBP and HOG

A novel face recognition method based on fusion of LBP and HOG

Recognizing Face Using the Combination of Singular Value Decomposition and Hidden Markov Model Algorithms

Face Recognition Attendance System Method Based on Fusion of LBP and HOG

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