Facial Expression Recognition Based on Local Phase Quantization and Sparse Representation

Wang, Zhen; Ying, Zilu

doi:10.1109/icnc.2012.6234551

Cited by 37 publications

(13 citation statements)

References 7 publications

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“…Jabid and Chae [46] introduce LBP-based Local Directional Pattern (LDP) algorithm, which is robust to illumination and has relatively low computational complexity. In addition, Local Phase Quantisation (LPQ) [47] is mainly based on short-time Fourier Transform and is stable in feature extraction. In [48], the improved es-LBP (expression-specific LBP) feature is proposed to extract the spatial information, the cr-LPP (class-regularised Locality Preserving Projection) method is proposed to simultaneously maximise the class independence and preserve the local feature similarity.…”

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

“…SRC [40,47,77,78] has better recognition effect than the traditional method, especially when the sample is subjected to random pixel corruption or random block occlusion. However, when handling data having the same direction distribution, SRC may not classify the data, since the sample vectors of different classes are distributed on the same vector direction.…”

Section: Expression Classificationmentioning

confidence: 99%

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Facial Expression Recognition: A Survey

et al. 2019

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Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.

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Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Section: Expression Classificationmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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“…Detailed survey of different approaches in each of these steps can be found in [44]. Conventional algorithms for affective computing from faces use engineered features such as Local Binary Patterns (LBP) [47], Histogram of Oriented Gradients (HOG) [8], Local Phase Quantization (LPQ) [59], Histogram of Optical Flow [9], facial landmarks [6,7], and PCA-based methods [36]. Since the majority of these features are hand-crafted for their specific application of recognition, they often lack required generalizability in cases where there is high variation in lighting, views, resolution, subjects' ethnicity, etc.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks

Hasani

Mahoor

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

218

150

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Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.

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“…In the literature, there are many approaches to extract the facial expression features from the face. The appearance-based feature descriptors are among the most successful methods such as local binary pattern (LBP) [18], local mean binary pattern [19], local Gabor binary patterns [20], LPQ [21], local directional texture pattern (LDTP) [22] and Gabor wavelet [23]. Another feature extraction approach is to use the shape information by using the distances and angles between the facial landmarks.…”

Section: Related Workmentioning

confidence: 99%