用于微表情识别的自适应位移生成与Transformer融合特征表示学习
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition
AI总结：
针对微表情识别中目标特征学习与高效融合不足的问题，提出FRL-DGT框架，利用自适应位移生成模块提取动态特征，并结合Transformer融合机制提取多层次信息特征，实验证明其优于现有方法。
摘要：
微表情是自发的、快速且细微的面部运动，既无法伪造也无法抑制。它们是非常重要的非言语交流线索，但由于具有瞬时性和低强度，因此难以识别。近年来，基于深度学习的方法利用特征提取和融合技术被开发用于微表情（ME）识别，然而，针对ME特性的目标特征学习和高效特征融合仍缺乏进一步研究。为了解决这些问题，我们提出了一种新颖的框架——自适应位移生成与Transformer融合的特征表示学习（FRL-DGT），其中使用具有自监督学习的卷积位移生成模块（DGM）从起始/峰值帧中提取针对后续ME识别任务的动态特征，并应用由三个基于Transformer的融合模块（基于AU区域的局部、全局融合以及全脸融合）组成的精心设计的Transformer融合机制，在DGM之后提取多层次信息特征以进行最终的ME预测。采用严格的留一受试者（LOSO）评估的广泛实验结果证明了我们提出的FRL-DGT优于现有最先进的方法。
英文摘要：
Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
