Semantic Pixel Sets Based Local Binary Patterns for Face Recognition

Chai, Zhenhua; Méndez-Vázquez, Heydi; He, Ran; Sun, Zhenan; Tan, Tieniu

doi:10.1007/978-3-642-37444-9_50

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…For example, Cament et al [30] combined entropy-like weighted Gabor features with the local normalisation of Gabor features. Chai et al [31] introduced the entropy of a facial region, where a low entropy value means the probabilities of different intensities are different, and a high value means the probabilities are the same. They used the entropy of each of the equal-size blocks of a face image to determine the number of sub-blocks within each block.…”

Section: Related Workmentioning

confidence: 99%

“…They used the entropy of each of the equal-size blocks of a face image to determine the number of sub-blocks within each block. Inspired by [31], we use entropy in the proposed MHI OF as follows. Since the intensity value of each pixel in MHI represents a movement, the high intensity values denoting large movement will result in high entropy value, and vice versa.…”

Section: Related Workmentioning

confidence: 99%

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A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Fan

Tjahjadi

2017

Pattern Recognition

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A dynamic descriptor facilitates robust recognition of facial expressions in video sequences. The current two main approaches to the recognition are basic emotion recognition and recognition based on facial action coding system (FACS) action units. In this paper we focus on basic emotion recognition and propose a spatiotemporal feature based on local Zernike moment in the spatial domain using motion change frequency. We also design a dynamic feature comprising motion history image and entropy. To recognise a facial expression, a weighting strategy based on the latter feature and sub-division of the image frame is applied to the former to enhance the dynamic information of facial expression, and followed by the application of the classical support vector machine. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state-of-arts methods, the proposed framework demonstrates a superior performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Fan

Tjahjadi

2017

Pattern Recognition

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“…Recently, the semantic pixel set-based LBP (spsLBP) [17] was proposed for this aim. By clustering the pixels in an image region into a number of sets according to their semantic meanings instead of using a regular division, it http://jivp.eurasipjournals.com/content/2014/1/26 makes better use of the spatial information when constructing the local histograms.…”

Section: Introductionmentioning

confidence: 99%

“…By clustering the pixels in an image region into a number of sets according to their semantic meanings instead of using a regular division, it http://jivp.eurasipjournals.com/content/2014/1/26 makes better use of the spatial information when constructing the local histograms. It was shown in [17] that this strategy can alleviate to some extent the pixel-shifting problem caused by some face deformations like variations in expression. However, only the original LBP operator was tested with the proposed strategy in [17], while more robust LBP variants can be used for improving the overall performance.…”

Section: Introductionmentioning

confidence: 99%

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Explore semantic pixel sets based local patterns with information entropy for face recognition

Chai

Méndez-Vázquez

et al. 2014

J Image Video Proc

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Several methods have been proposed to describe face images in order to recognize them automatically. Local methods based on spatial histograms of local patterns (or operators) are among the best-performing ones. In this paper, a new method that allows to obtain more robust histograms of local patterns by using a more discriminative spatial division strategy is proposed. Spatial histograms are obtained from regions clustered according to the semantic pixel relations, making better use of the spatial information. Here, a simple rule is used, in which pixels in an image patch are clustered by sorting their intensity values. By exploring the information entropy on image patches, the number of sets on each of them is learned. Besides, Principal Component Analysis with a Whitening process is applied for the final feature vector dimension reduction, making the representation more compact and discriminative. The proposed division strategy is invariant to monotonic grayscale changes, and shows to be particularly useful when there are large expression variations on the faces. The method is evaluated on three widely used face recognition databases: AR, FERET and LFW, with the very popular LBP operator and some of its extensions. Experimental results show that the proposal not only outperforms those methods that use the same local patterns with the traditional division, but also some of the best-performing state-of-the-art methods.

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Dual Gradient Feature Pair Based Face Recognition for Aging and Pose Changes

Shoba

Sam

2021

Communications in Computer and Information Science

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This paper proposes a novel dual gradient feature pair (DGFP) based face recognition system that is robust against pose and age variation. This DGFP method, initially segments the rectangular face region into 6 regions which forms 3 pairs which is almost symmetrical to each other. From each pair highly matched HOG features are extracted and the dual gradient features like the positive gradients and negative gradients are extracted from the symmetrical regions of the face gets matched. DGFP is extracted in both the training and testing images. The matching is obtained using the minimum Euclidean distance with a threshold value. Experimental results are evaluated using the datasets such as MORPH, FGNET and Yale dataset and the results reveals that the proposed DGFP face recognition system has achieved an average recognition rate of 92.07%, average time of feature extraction is 253s and the average time of face recognition is 0.1533s

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Semantic Pixel Sets Based Local Binary Patterns for Face Recognition

Cited by 17 publications

References 17 publications

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Explore semantic pixel sets based local patterns with information entropy for face recognition

Dual Gradient Feature Pair Based Face Recognition for Aging and Pose Changes

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