A micro-expression is a fleeting, delicate and localized facial gesture. It can expose the true feelings that someone is trying to hide and is seen to be a crucial indicator for spotting lies. Because of its possible applications in a variety of sectors, micro-expression research has garnered a lot of attention. The accuracy of micro-expression recognition still needs to be improved, though, because of the brief and weak motions that make up micro-expressions. In recent years, Deep convolution neural methods have depicted a higher degree of efficiency for complex challenge of face detection. Although several attempts were made for micro-expression recognition (MER), the problem is far from being resolved problem which is portrayed by the lowest accuracy rate depicted by the other models. In this study, present a Facial Micro-Expression Detection and Classification using Modified Multimodal Ensemble Learning (FMEDC-MMEL) approach. The major intention of the FMEDC-MMEL technique lies in the proficient identification of MEs that exist in the facial images. As a pre-processing phase, the FMEDC-MMEL technique exploits histogram equalization (HE) approach to improve the contrast level of the image. In the FMEDC-MMEL technique, improved densely connected networks (DenseNet) model is used for learning feature patterns from the pre-processed images. To enhance the proficiency of the improved DenseNet model, stochastic gradient descent (SGD) approach is used for hyperparameter selection process. For facial ME detection, the FMEDC-MMEL technique follows an ensemble of three classifiers namely bi-directional gated recurrent unit (Bi-GRU), long short-term memory (LSTM) and extreme learning machine (ELM). A tailored ensemble learning approach is shown, which combines many machine learning models to improve classification performance and detection accuracy. Sophisticated feature extraction methods are utilized to extract the subtle aspects of micro-expressions, and precision is maintained by optimizations that minimize computing cost. Empirical findings reveal that this methodology notably surpasses conventional techniques, providing enhanced precision and resilience on a variety of complex and demanding datasets. In addition to pushing the boundaries of micro-expression analysis research, the proposed strategy has potential uses in the real world in fields including security, psychology testing, and human-computer interaction.

中文翻译：

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使用改进的多模态集成学习方法进行面部微表情检测和分类


微表情是一种转瞬即逝、细腻且本地化的面部手势。它可以暴露某人试图隐藏的真实感受，并被视为发现谎言的关键指标。由于其可能应用于各个领域，微表达研究引起了很多关注。不过，由于构成微表情的动作简短而微弱，微表情识别的准确性仍有待提高。近年来，深度卷积神经方法已经描述了更高的效率来应对复杂的人脸检测挑战。尽管对微表情识别 （MER） 进行了多次尝试，但问题远未得到解决，其他模型描述的准确率最低。在本研究中，提出了一种使用改进的多模态集成学习 （FMEDC-MMEL） 方法的面部微表情检测和分类。FMEDC-MMEL 技术的主要意图在于熟练识别面部图像中存在的 ME。作为预处理阶段，FMEDC-MMEL 技术利用直方图均衡 （HE） 方法来提高图像的对比度水平。在 FMEDC-MMEL 技术中，改进的密集连接网络 （DenseNet） 模型用于从预处理图像中学习特征模式。为了提高改进的 DenseNet 模型的熟练程度，随机梯度下降 （SGD） 方法用于超参数选择过程。对于面部 ME 检测，FMEDC-MMEL 技术遵循三个分类器的集成，即双向门控循环单元 （Bi-GRU）、长短期记忆 （LSTM） 和极限学习机 （ELM）。 展示了一种定制的集成学习方法，该方法结合了许多机器学习模型来提高分类性能和检测准确性。利用复杂的特征提取方法来提取微表达式的细微方面，并通过最小化计算成本的优化来保持精度。实证结果表明，这种方法明显优于传统技术，在各种复杂和要求苛刻的数据集上提供了更高的精度和弹性。除了突破微表情分析研究的界限外，所提出的策略在现实世界中还有潜在的用途，包括安全、心理学测试和人机交互。