Facial expression recognition by fusion of gabor texture features and local phase quantization

Li, Lisai; Ying, Zilu; Yang, Tairen

doi:10.1109/icosp.2014.7015300

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Therefore, to obtain a high recognition rate, the current research work on facial expression recognition first adopts a feature extraction algorithm, and then according to a specific task, designs a model of expression recognition. Six kinds of texture feature extraction operators are verified to have a superior performance in facial feature [5], LPQ [9], Gabor, and HOG.…”

Section: Appearance-based Feature Extraction Of Facial Expressionmentioning

confidence: 99%

Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition

Wang

Wan

2023

J. Phys.: Conf. Ser.

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Extracting effective features of expressions becomes a hot research topic, and a single feature pattern cannot reflect the diversity of expressions. Therefore, to obtain rich information feature data and raise the expression recognition performance, we propose a feature fusion model of multiple feature selection by the measure of the RV correlation coefficient. In the proposed feature fusion model, the feature patterns are firstly selected by RV correlation coefficient from various expression texture features. And then according to rank the values of the RV correlation coefficient, we build a CCA subspace and PCA subspace respectively to fuse selected features. Finally, a new facial expression feature presentation is constructed through weighting and combining the two fusion features from the subspaces. The new features are fed to SVM classifier for expression recognition. Experimental verification shows that our proposed model has a superior performance than the existing algorithms.

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Section: Appearance-based Feature Extraction Of Facial Expressionmentioning

confidence: 99%

Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition

Wang

Wan

2023

J. Phys.: Conf. Ser.

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“…Specifically, LPQ features are acquired from the frequency domain and LTP features are selected from the spatial domain. Actually, the LPQ method [39] can effectively describe texture features of image and has been used in target tracking [40], face recognition [41, 42], and so on. In general, the LPQ method can be obtained by calculating the local phase information of image.…”

Section: Proposed Schemementioning

confidence: 99%

A novel hashing scheme via image feature map and 2D PCA

Liang

Tang

Sheng

et al. 2022

IET Image Processing

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Hashing scheme is a high‐efficiency technique for processing massive images. Two critical metrics of the hashing scheme are discrimination and robustness, but most schemes do not get satisfied classification performance between them. This paper proposes a novel hashing scheme via image feature map and 2D PCA. First, the proposed scheme extracts local phase quantization (LPQ) features in the frequency domain and local ternary pattern (LTP) features in the spatial domain, and combines them to construct an image feature map. Second, the proposed scheme conducts dimension reduction via 2D PCA for learning features from the image feature map. Last, the learned features are compressed to generate the hash sequence. Performances are tested on open image datasets. The results demonstrate that the proposed scheme can make a good balance between discrimination and robustness. In addition, the classification and copy detection of the proposed scheme are both superior to those of some famous hashing schemes.

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“…The selection of W is highly important as it determines what information is going to be extracted. The filtered image I 0 can be obtained by convoluting the original image with Gabor function [ 27 , 28 ],

can be expressed:

.…”

Section: Image Feature Extractionmentioning

confidence: 99%

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection

Yang

et al. 2020

Sensors

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In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

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Facial expression recognition by fusion of gabor texture features and local phase quantization

Cited by 10 publications

References 7 publications

Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition

Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition

A novel hashing scheme via image feature map and 2D PCA

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection

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