Department of Electronics and Communication Engineering, National Institute of Technology, Warangal 506004, India.
Viume, Hyderabad, India.
Sensors (Basel). 2021 Feb 5;21(4):1098. doi: 10.3390/s21041098.
Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.
面部微表情是短暂的、自发的、内心深处的关键情绪，反映了当时的真实想法。人类可以在很大程度上掩盖自己的情绪，但他们的真实意图和情绪可以在微观层面上被提取出来。与宏表情相比，微表情是有机的，这对人类和机器来说都是一个挑战。近年来，面部表情的检测被广泛应用于商业综合体、酒店、餐厅、心理学、安全、办公室和教育机构。本文的目的和动机是提供一种端到端的架构，能够准确地检测微观特征的实际表情。然而，主要的研究是提供一个分析，以确定从面部检测微表情的关键特定部分。许多最先进的方法已经在微面部表情上进行了训练，并与我们提出的无损失注意力残差网络（LARNet）方法进行了比较。然而，主要的研究是提供一个分析，以确定从面部检测微表情的关键特定部分。许多基于 CNN 的方法从局部水平提取特征，更深入地挖掘面部像素。然而，LARNet 对人脸进行特征融合提取时，在特定的关键位置（如鼻子、脸颊、嘴巴和眼睛区域）编码了从人脸中提取的空间和时间信息。LARNet 通过实时准确地检测面部微表情，以微小的优势超过了最先进的方法。最后，通过使用更多标注数据进行训练，所提出的 LARNet 变得更加准确和有效。
