Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System
Owing to the development and advancement of artificial intelligence, numerous works were established in the human facial expression recognition system. Meanwhile, the detection and classification of micro-expressions are attracting attentions from various research communities in the recent few years. In this paper, we first review the processes of a conventional optical-flow-based recognition system, which comprised of facial landmarks annotations, optical flow guided images computation, features extraction and emotion class categorization. Secondly, a few approaches have been proposed to improve the feature extraction part, such as exploiting GAN to generate more image samples. Particularly, several variations of optical flow are computed in order to generate optimal images to lead to high recognition accuracy. Next, GAN, a combination of Generator and Discriminator, is utilized to generate new "fake" images to increase the sample size. Thirdly, a modified state-of-the-art Convolutional neural networks is proposed. To verify the effectiveness of the the proposed method, the results are evaluated on spontaneous micro-expression databases, namely SMIC, CASME II and SAMM. Both the F1-score and accuracy performance metrics are reported in this paper.
Code (0)
등록된 구현이 없습니다.
Tasks
Facial Expression RecognitionFacial Expression Recognition (FER)Micro Expression RecognitionMicro-Expression RecognitionOptical Flow EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Spontaneous Facial Micro-Expression Recognition using Discriminative Spatiotemporal Local Binary Pattern with an Improved Integral Projection
Recently, there are increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for sp…
Attributefeature selectionMicro Expression RecognitionMicro-Expression RecognitionShort and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition
Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensity, the recognition of micro-expressions…
Micro Expression RecognitionMicro-Expression RecognitionRelationSpatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions
Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of …
Data AugmentationMicro Expression RecognitionMicro-Expression RecognitionMERANet: Facial Micro-Expression Recognition using 3D Residual Attention Network
Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still …
Micro Expression RecognitionMicro-Expression RecognitionSpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting
Facial expression spotting, identifying periods where facial expressions occur in a video, is a significant yet challenging task in facial expression analysis. The issues of irrelevant facial movements and the challenge …
Contrastive LearningMicro-Expression SpottingOptical Flow Estimation