Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns

Faraji, Mohammad Reza; Qi, Xiaojun

doi:10.1049/iet-cvi.2014.0200

Cited by 33 publications

(19 citation statements)

References 24 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, A, B, and C clusters were obtained by using the proposed triangular-based fuzzy method. It should be expressed that the triangle-based fuzzy method is a modified version of Neutrosophy theory [28]. Then, the proposed LCPs, LBP, and the other 9 graph structures were applied to the B cluster.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

View full text Add to dashboard Cite

In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To further verify the advantages of the proposed method, the performance of the proposed method was compared with two histogram based techniques, HE [8] and CLAHE [11]; three Retinex theory based techniques, SSR [13], MSR [17] and SSR + BF [14]; and two transform domain based techniques, HF [19] and UIC [21]. The most representative image was selected from each type of image (four types) as the test image for further analysis.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Huang

et al. 2018

Symmetry

View full text Add to dashboard Cite

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3–4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

show abstract

“…where D(u,v) is the distance from (u,v) to the original of the center Fourier transform, D 0 is the cutoff distance measured from the origin, g L < 1 and g H > 1 are the parameters of the filter, and c is a constant to control the sharpness of the slope of the filter function as transition take place. Literature by Faraji and Qi (2015) has proven that c is the key parameter of the homomorphic filter. Every ground wire image has different unknown amount of illuminations that require a different filter to compensate illumination.…”

Section: Pre-processing Based On Adaptive Homomorphic Filtermentioning

confidence: 99%

Overhead ground wire detection by fusion global and local features and supervised learning method for a cable inspection robot

Fan

et al. 2018

View full text Add to dashboard Cite

Purpose-An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection. Design/methodology/approach-First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed. Findings-Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo. Originality/value-This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

show abstract

Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns

Cited by 33 publications

References 24 publications

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Overhead ground wire detection by fusion global and local features and supervised learning method for a cable inspection robot

Contact Info

Product

Resources

About