Learning effective binary descriptors for micro-expression recognition transferred by macro-information

Ben, Xianye; Jia, Xitong; Yan, Rui; Zhang, Xin; Meng, Weixiao

doi:10.1016/j.patrec.2017.07.010

Cited by 51 publications

(20 citation statements)

References 25 publications

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“…Machine learning-based methods 243 Long-range iris recognition 244 Fingerprint recognition Fingerprint recognition for young children 245 Fingerprint recognition at crime scenes 246 Review works 247,248 Others Age and gender recognition 249,250 Facial expression recognition À À À CNN based methods 252,253 Multi-modality feature fusion-based method 254 Expression recognition based on static images 255 Micro-Expression Recognition [256][257][258] Facial expressions generation À À À Interactive GAN-based method 260 3D facial expression generation 261 Humanoid robot expression generation 23 Three-dimensional speaking characters 262 Expression generation natural description 264,265 Posture or gestures recognition À À À Driving posture recognition 266 Weighted fusion method for gesture recognition 267 Posture recognition for hazard prevention 268 Emotional body gesture recognition 269 Gesture recognition in video 271 Hand gesture recognition 272 deep neural network methods are introduced into the¯eld to seek a better recognition performance. The¯rst step of face recognition is the detection of face with an aim to determine whether faces exist on a given image or not.…”

Section: Iris Recognitionmentioning

confidence: 99%

“…255 Micro-expression recognition, which is regarded as a harder problem, was also researched by a large amount of research works. [256][257][258] Corresponding to facial recognition, this study provides the automatic generation of facial expressions. Its content generated various emotional expressions of a given facial image or a speci¯c text.…”

Section: Iris Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Human-Computer Interaction and Intelligent Robots

Ren

Bao

2020

Int. J. Info. Tech. Dec. Mak.

100

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In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

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Section: Iris Recognitionmentioning

confidence: 99%

Section: Iris Recognitionmentioning

confidence: 99%

A Review on Human-Computer Interaction and Intelligent Robots

Ren

Bao

2020

Int. J. Info. Tech. Dec. Mak.

100

View full text Add to dashboard Cite

show abstract

“…Since they did not take the gap between micro-expression and macro-expression images into account, transfer learning did not achieve the desired effect. Jia et al [10] and Ben et al [1] extracted LBP-TOP features from micro-expression images and LBP features from macro-expression images. Jia et al used singular value decomposition to achieve macro-to-micro transformation model, while Ben et al employed coupled metric learning algorithm to model the shared features between micro-expression and macro-expression samples.…”

Section: Related Work 21 Micro-expression Recognitionmentioning

confidence: 99%

Learning from Macro-expression

Xia

Wang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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As one of the most important forms of psychological behaviors, micro-expression can reveal the real emotion. However, the existing labeled micro-expression samples are limited to train a high performance micro-expression classifier. Since micro-expression and macro-expression share some similarities in facial muscle movements and texture changes, in this paper we propose a microexpression recognition framework that leverages macro-expression samples as guidance. Specifically, we first introduce two Expression-Identity Disentangle Network, named MicroNet and MacroNet, as the feature extractor to disentangle expression-related features for micro and macro expression samples. Then MacroNet is fixed and used to guide the fine-tuning of MicroNet from both label and feature space. Adversarial learning strategy and triplet loss are added upon feature level between the MicroNet and MacroNet, so the MicroNet can efficiently capture the shared features of microexpression and macro-expression samples. Loss inequality regularization is imposed to the label space to make the output of Mi-croNet converge to that of MicroNet. Comprehensive experiments on three public spontaneous micro-expression databases, i.e., SMIC, CASME2 and SAMM demonstrate the superiority of the proposed method. CCS CONCEPTS • Human-centered computing → HCI design and evaluation methods.

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“…Huang and Zhao (2017) proposed a new binary pattern variant called spatio-temporal local Radon binary pattern (STRBP) that uses Radon transform to obtain robust shape features. Ben et al (2017) proposed an alternative binary descriptor called Hot Wheel Patterns (HWP) (and its spatio-temporal extension HWP-TOP) to encode the discriminative features of both macro- and micro-expressions images. A coupled metric learning algorithm is then used to model the shared features between micro- and macro-expression information.…”

Section: Recognition Of Facial Micro-expressionsmentioning

confidence: 99%

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

et al. 2018

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Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.

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Learning effective binary descriptors for micro-expression recognition transferred by macro-information

Cited by 51 publications

References 25 publications

A Review on Human-Computer Interaction and Intelligent Robots

A Review on Human-Computer Interaction and Intelligent Robots

Learning from Macro-expression

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

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