Facial expression recognition using intra‐class variation reduced features and manifold regularisation dictionary pair learning

Xie, Siyue; Hu, Haifeng; Yin, Ziyu

doi:10.1049/iet-cvi.2017.0422

Cited by 11 publications

(4 citation statements)

References 49 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Table 1, the TSDN method is optimal at 100 epochs of two‐stream encoder‐decoder networks and a difference network. In evaluating micro‐expression recognition, to deal with the imbalanced class distribution, we also use the accuracy and F1_score [24] for performance evaluation. Specifically, F1_score is expressed as:

F 1_s c o r e = 2 \times \frac{P r e c i s i o n \times R e c a l l}{P r e c i s i o n + R e c a l l}

…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Micro‐expression recognition by two‐stream difference network

Pan

Xie

et al. 2021

IET Computer Vision

View full text Add to dashboard Cite

Facial micro-expression is a superposition of micro-expression features and identity information of a subject. For the problem of identity information interference in microexpression recognition, this study proposes a new method for facial micro-expression recognition by de-identity information, called two-stream difference network (TSDN). First, a two-stream encoder-decoder network is trained by a convolutional neural network, where the input of the micro-expression stream is a micro-expression image, and the identity stream is a facial identity image. The micro-expression image is the apex image, and the identity image is the onset image in the micro-expression sequence. The identity information and micro-expression features are recorded in the intermediate layer of the micro-expression stream, while the intermediate layer of the identity stream contains only the identity information of a subject. Then, the identity information is removed by the difference network, but micro-expression features are stored in the intermediate layer of the micro-expression stream. Given the sequence of the microexpressions, the TSDN model of de-identity information learns the difference that stores in the expression stream. Two public spontaneous facial micro-expression data sets (SMIC and CASME II) are employed in our experiments. The experiment results show that our model can achieve a superior performance in micro-expression recognition.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

F 1_s c o r e = 2 \times \frac{P r e c i s i o n \times R e c a l l}{P r e c i s i o n + R e c a l l}

…”

Section: Resultsmentioning

confidence: 99%

“…Some research works have achieved surprising results in improving macro-expression recognition by considering the identity information of subjects such as age, gender, and ethnic background [21][22][23][24]. However, these methods do not apply to micro-expression recognition.…”

Section: Introductionmentioning

confidence: 99%

Micro‐expression recognition by two‐stream difference network

Pan

Xie

et al. 2021

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…The research 18 , 40 – 42 showed that it is helpful to improve the discrimination capability of dictionary by introducing the local structure information of samples in the DL process. In general, the scholars preserve the local structure of samples using a graph Laplacian matrix that is defined by k-nearest neighbors or ε-nearest neighbors.…”

Section: Joint Structured Constraint Discriminant Analysis Dictionary...mentioning

confidence: 99%

Joint structured constraint discriminant analysis dictionary learning for pattern classification

Zhang

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

.Analysis dictionary learning (DL) has been successfully applied to the field of pattern classification. However, it is still a challenge to utilize the local structure information and the class information of samples to improve the discrimination capability of analysis dictionary. We proposed a joint structured constraint discriminant analysis DL (ADL) method (JSCDADL) to learn a structured discriminant analysis dictionary by combining the local structure information and the structured information of samples. Specifically, we first designed an adaptive local structure preserving term (ALSPT) to improve the discrimination capability of analysis dictionary. It adaptively transmits the local structure information of samples to analysis dictionary, which ensures that the same class of samples has similar sparse codes under the action of the analysis dictionary. Then, we designed a discriminative sparse coding error term that forces the coding coefficient matrix to have the desired block diagonal structure. To further enhance the discrimination capability of analysis dictionary, we designed an analysis dictionary combination term by constantly approximating the two analysis dictionaries learned to obtain an analysis dictionary with the local structure information and the structured information of samples. Moreover, we designed an effective iterative algorithm to solve the optimization problem of JSCDADL. Extensive experimental results on six datasets demonstrate that JSCDADL can achieve satisfactory classification performance compared with some state-of-the-art methods.

show abstract

“…The second is non-permanent changes. For example, illumination and posture vary from image to image, temporary decorations and stains may appear on the skin on different acquisition sessions [3]- [5]. For the intra-class variations that are not included in the template set, the systems are very likely to reject genuine users falsely.…”

Section: Introductionmentioning

confidence: 99%

A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems

Chen

Feng

et al. 2019

IEEE Access

View full text Add to dashboard Cite

It is already a consensus that intra-class variations will decline the performance of biometric systems. Many studies have explained how intra-class variations affect the biometric systems, while few approaches have proposed the effective solutions of template update. Since template update is a crucial procedure in biometric recognition systems, the problem is challenging because the newly selected templates must be representative of a large amount of intra-class variations such as posture changes and lighting conditions. To this end, existing techniques proposed to perform template update by periodically selecting new representative templates. However, all of them are designed for specific biometric traits rather a generic framework, and they are either ineffective because selected templates are less representative or prone to selection errors due to the presence of outliers. In this paper, we propose a novel generic template update framework. We use pass tables to accumulate information for outlier removal and representative templates selection, and further, propose a novel criterion to select the optimal template set according to current templates and similarity threshold. To confirm the effectiveness of our method, we carry out several experiments on synthetic and real datasets from different biometric traits. The results show that the proposed method is more robust than existing ones, and at the same time, achieves a higher accuracy according to the specified performance indicator. It also demonstrated the superiority of our method to random selection for different biometric traits. INDEX TERMSIntra-class variations, pass table, template selection, template update.

show abstract

Facial expression recognition using intra‐class variation reduced features and manifold regularisation dictionary pair learning

Cited by 11 publications

References 49 publications

Micro‐expression recognition by two‐stream difference network

Micro‐expression recognition by two‐stream difference network

Joint structured constraint discriminant analysis dictionary learning for pattern classification

A Generic Template Update Framework: The Solution for Intra-Class Variations in Biometric Systems

Contact Info

Product

Resources

About