Face recognition using augmented local binary pattern and Bray Curtis dissimilarity metric

Shyam, Radhey; Singh, Yatindra Nath

doi:10.1109/spin.2015.7095267

Cited by 25 publications

(16 citation statements)

References 32 publications

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“…Table 7 compares the Maximum %RR of other algorithms with proposed work. It is justified that our work is outperforming in terms of Maximum %RR compared to [31] and [32], but comparable with [33], which has marginal difference of 0.67.…”

Section: A Performance On Yale Databasementioning

confidence: 73%

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Synergy of Schur, Hessenberg and QR Decompositions on Face Recognition

Jagadeesh¹,

K²,

Raja³

2016

IJIGSP

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Human recognition through faces has elusive challenges over a period of time. In this paper, an efficient method using three matrix decompositions for face recognition is proposed. The proposed model uses Discrete Wavelet Transform (DWT) with Extended Directional Binary codes (EDBC) in one branch. Three matrix decompositions combination with Singular Value Decomposition (SVD) is used in the other branch. Preprocessing uses Single Scale Retinex (SSR), Multi Scale Retinex (MSR) and Single scale Self Quotient (SSQ) methods. The Approximate (LL) band of DWT is used to extract one hundred EDBC features. In addition, Schur, Hessenberg and QR matrix decompositions are applied individually on pre-processed images and added. Singular Value Decomposition (SVD) is applied on the decomposition sum to yield another one hundred features. The combination EDBC and SVD features are final features. City-block or Euclidean Distance (ED) measures are used to generate the results. Performance on YALE, GTAV and ORL face datasets is better compared to other existing methods.

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Section: A Performance On Yale Databasementioning

confidence: 73%

“…ED is used to compute the results on GTAV face database. [31] 71.9 WM(2D) 2 PCA [32] 80.77 GABOR +DTW [33] 90.67 Proposed MDE-D3S FR model 90 Table 8. Performance on GTAV Database with Pre-processing Methods…”

Section: B Performance On Gtav Databasementioning

confidence: 99%

Synergy of Schur, Hessenberg and QR Decompositions on Face Recognition

Jagadeesh¹,

K²,

Raja³

2016

IJIGSP

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show abstract

“…Bray-Curtis dissimilarity is a modified way of the Manhattan dissimilarity measure. Equation 13shows the general equation of Bray-Curtis dissimilarity [29]:…”

Section: Bray-curtismentioning

confidence: 99%

FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels

Mukhopadhaya

Kumar

Stein³

2018

Remote Sensing

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In this paper, the fuzzy c-means (FCM) classifier has been studied with 12 similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray–Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference, Euclidean, Mahalanobis, diagonal Mahalanobis and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m*) and also at different α-cuts. The two best single measures obtained were combined to study the effect of composite measures on the datasets used. An image-to-image accuracy check was conducted to assess the accuracy of the classified images. Fuzzy error matrix (FERM) was applied to measure the accuracy assessment outcomes for a Landsat-8 dataset with respect to the Formosat-2 dataset. To conclude, FCM classifier with Cosine measure performed better than the conventional Euclidean measure. But, due to the incapability of the FCM classifier to handle noise properly, the classification accuracy was around 75%.

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“…Bray-Curtis dissimilarity is a modified way of the Manhattan dissimilarity measure. Equation 1.10 shows the general equation of Bray-Curtis dissimilarity [29]:…”

Section: Bray-curtismentioning

confidence: 99%

An Effective FCM Approach of Similarity and Dissimilarity Measures with Alpha-Cut

Mukhopadhaya¹,

Kumar²,

Stein³

2018

Preprint

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In this study, the fuzzy c- means classifier has been studied with nine other similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray-Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m) and also at different α-cuts. The two best single norms obtained were combined to study the effect of composite norms on the datasets used. An image to image accuracy check was conducted to assess the accuracy of the classified images. Fuzzy Error Matrix (FERM) was applied to measure the accuracy assessment outcomes for a Landsat-8 dataset with respect to the Formosat-2 dataset. To conclude FCM classifier with Cosine norm performed better than the conventional Euclidean norm. But, due to the incapability of the FCM classifier to handle noise properly, the classification accuracy was around 75%.

show abstract

Face recognition using augmented local binary pattern and Bray Curtis dissimilarity metric

Cited by 25 publications

References 32 publications

Synergy of Schur, Hessenberg and QR Decompositions on Face Recognition

Synergy of Schur, Hessenberg and QR Decompositions on Face Recognition

FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels

An Effective FCM Approach of Similarity and Dissimilarity Measures with Alpha-Cut

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