Multi-modality Network with Visual and Geometrical Information for Micro Emotion Recognition

Guo, Jianzhu; Zhou, Shuai; Wu, Jinlin; Wan, Jun; Zhu, Xiangyu; Lei, Zhen; Li, Stan Z.

doi:10.1109/fg.2017.103

Cited by 37 publications

(36 citation statements)

References 8 publications

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“…Affective geometric features are extracted using the warp transformation of facial landmarks to capture the configuration of facial landmark in [4]. Facial landmark with 68 points is described as geometrical representation of face [16].…”

Section: Geometrical Featuresmentioning

confidence: 99%

“…To extract the dynamic texture features from the video, [4] used histogram of oriented gradients from three orthogonal planes (HOG-TOP). The visual features are extracted from the color image using convolutional neural network (CNN) as a feature descriptor in [16]. The effects of the approaches are time-consuming, and the characteristic dimension is huge, so the dimensionality reduction methods are used to affect the accuracy of facial expression recognition.…”

Section: Apperance Featuresmentioning

confidence: 99%

“…The feature vector is fed to the hinge loss layer. The framework is better performance than CNN with the misclassification of 80.212137 using caffe [16]. Also proposed another three works of the work-shop.…”

Section: Introductionmentioning

confidence: 97%

“…In [9, 13,15,[23][24] have classified one more class as neutral and [8,17,23] have done contempt class. All of eight classes have been classified by the studies in [11,10,16]. However, [21] and [22] have worked on neutral, happiness, surprise and disgust expressions.…”

Section: Introductionmentioning

confidence: 99%

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Feature extraction and classification methods of facial expression: a survey

Htay

2021

Comput. Sci. Inf. Technol.

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Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial Expression Recognition System (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

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Section: Geometrical Featuresmentioning

confidence: 99%

Section: Apperance Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Feature extraction and classification methods of facial expression: a survey

Htay

2021

Comput. Sci. Inf. Technol.

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“…The most common applications cover phone unlock (e.g., iPhone X), access control, surveillance, and security. Face, as one of the biometric modalities, gains increasing popularity in academic and industry community [12,11]. Face recognition has achieved great success in terms of verification and identification [16,34,17] However, spoof faces can be easily obtained by printers (i.e., print attack) and digital camera devices (i.e., replay attack).…”

Section: Introductionmentioning

confidence: 99%

Improving Face Anti-Spoofing by 3D Virtual Synthesis

Guo

Zhu

Xiao

et al. 2019

2019 International Conference on Biometrics (ICB)

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Face anti-spoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need large-scale training samples to reduce overfitting. However, acquiring spoof data is very expensive since the live faces should be reprinted and re-captured in many views. In this paper, we present a method to synthesize virtual spoof data in 3D space to alleviate this problem. Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space. Afterward, the transformed 3D photo is rendered through perspective projection as a virtual sample. The synthetic virtual samples can significantly boost the anti-spoofing performance when combined with a proposed data balancing strategy. Our promising results open up new possibilities for advancing face anti-spoofing using cheap and largescale synthetic data.

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The Influence of Facial Width-to-Height Ratio on Micro-expression Recognition

Zhang

Xie

2018

Intelligence Science II

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The aim of the present study was to uncover the potential impact of facial width-to-height ratio (fWHR) on micro-expression and macro-expression recognition. The JACBART paradigm was used for the presentation of facial expressions. Participants were asked to recognize six kinds of basic expressions (sadness, surprise, anger, disgust, fear, happiness) on high fWHR faces or on low fWHR faces under 67ms, 333ms and 600ms duration conditions respectively. The results indicated that, the fWHR did not affect the recognition of macroexpressions which were presented for 600ms in the present study, but the fWHR could influence the recognition accuracy of micro-expressions of surprise and happiness. Specifically, participants could identify the facial expression of surprise more effectively on high fWHR faces than on low fWHR faces under the condition of 67ms. And participants also could recognize the facial expression of happiness more accurately on high fWHR faces in the conditions of 67ms and 333m. These results revealed the facial expressions of happiness and surprise on high fWHR faces may have an early processing advantage in micro-expression recognition. And the result also demonstrated that individuals spontaneously use fWHR as a clue to recognize micro-expressions.

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Multi-modality Network with Visual and Geometrical Information for Micro Emotion Recognition

Cited by 37 publications

References 8 publications

Feature extraction and classification methods of facial expression: a survey

Feature extraction and classification methods of facial expression: a survey

Improving Face Anti-Spoofing by 3D Virtual Synthesis

The Influence of Facial Width-to-Height Ratio on Micro-expression Recognition

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