# Attention-Based Spatiotemporal-Aware Network for Fine-Grained Visual Recognition
## 1 Introduction  
Facial expressions convey a wealth of communicative information, often surpassing that conveyed through language and bodily cues. Numerous studies have contributed to facial expression recognition; however, many of these primarily focus on extracting coarse-grained macroscopic features of facial expressions, potentially overlooking subtle, brief facial information. In this paper, we take micro-expressions as an example to delve into the exploration of visual recognition methods based on fine-grained features. When human beings hide real emotions, subtle changes will occur in facial muscles. These changes result in transient and involuntary facial expressions. A micro-expression has the characteristics of real emotional reflection and is difficult to forge. Therefore, it plays an important role in the fields of medical diagnosis [1], lie detection [2], business negotiation [3] and so on. Due to micro-expression's small range and short duration, it is difficult to be captured and recognized by the naked eye. Even after professionaltraining, the accuracy of recognition by human eyes is only 47% [4]. Therefore, in the era of artificial intelligence, it is of great significance to study how to capture and recognize micro-expressions with the help of fast visual capture equipment and artificial intelligence technology. At present, the study of micro-expression is focused on video stream data. The existing methods mainly include: LBP (Local Binary Pattern) [5], optical flow and methods based on Deep Learning [6]. LBP can describe the local texture features of the image and extract them, which has the advantages of gray invariance and rotation invariance. LBP usually extracts the appearance and motion features from the symbol-based difference between two pixels, so it will lose some information about features. In order to extract more useful information, the improved methods based on LBP consider the information of amplitude and direction, but this also leads to higher feature dimension after extraction. Most of these improved methods will reduce the processing speed and find it difficult to achieve real-time detection. Optical flow methods use the relevance between adjacent frames in an image sequence to calculate the motion information of objects. These methods can detect small motion changes, and are suitable for recognizing micro-expressions. Optical flow methods extract geometric features from images, so they have a relatively low dimension of features and a relatively fast processing speed. However, the effect of optical flow feature extraction will be greatly reduced in the scenes with large changes in brightness. Methods based on LBP or optical flow usually extract the shallow features of micro-expression images, so they cannot fully describe the structural information of samples and can hardly distinguish the relevance between high-dimensional features. Therefore, it is still a challenge to extract useful information and obtain high-quality description from micro-expressions. For micro-expression recognition, feature extraction is an important critical issue [7], especially for optimal flow-based micro-expression recognition. The methods based on deep learning can extract the deep features from micro-expression images and recognize them [8]. Due to the change in a micro-expression being relatively small, it is hard to distinguish the effectiveness of features by directly transforming its RGB image into matrix and inputting it into our network for convolution. Therefore, LBP or optical flow is usually used to extract the image's low-dimensional features before inputting them into the network to extract the deep-dimensional features. Due to the weak intensity of micro-expressions, directly inputting their RGB images into the network for convolution makes it difficult to distinguish effective features. Therefore, we use micro-expression videos as the model input.