A Comprehensive Study on Geometric, Appearance, and Deep Feature based Methods for Automatic Facial Expression Recognition

Naveen Kumar H N, C. Patil, Amith K. Jain, Sudheesh K V, Mahadevaswamy
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Abstract

Facial Expression (FE) is one kind of communication that, despite its non-verbal nature, predates verbal communication in terms of both its genesis and its conception. Automatic Facial Expression Recognition (AFER) is a predominant facet in analyzing facial images and thus has been an in-demand research problem for decades in the emerging field of Computer Vision (CV) & Artificial Intelligence (AI). Recent works on AFER systems focused on the following issues: insufficient training data which causes overfitting; robustness to identity bias, illumination & head pose variation, partial occlusion; generalization power; transformation from controlled to uncontrolled environments; cross dataset experiments. A comprehensive study on existing methods for the design and development of AFER systems is presented in the proposed study. The benchmark datasets and its characteristics are summarized. The advantages and limitations of the existing methods to extract the highly discriminative and abstract distributions are discussed. The evaluation methods to assess the performance of AFER systems, along with comparative analysis of various methods implemented on benchmark datasets are summarized. Furthermore, unresolved challenging issues in the field of AFER are presented in detail, which serves as an open-ended research area concerning the AFER problem.
基于几何、外观和深度特征的面部表情自动识别方法的综合研究
面部表情是一种交际方式,尽管它具有非语言的性质,但它的起源和概念都早于语言交际。自动面部表情识别(AFER)是人脸图像分析的一个主要方面,因此在计算机视觉(CV)和人工智能(AI)的新兴领域几十年来一直是一个迫切需要的研究问题。最近关于after系统的工作集中在以下问题上:训练数据不足导致过拟合;对识别偏差、光照和头姿变化、部分遮挡的鲁棒性;泛化能力;从受控环境到非受控环境的转变;跨数据集实验。本文对现有的设计和开发AFER系统的方法进行了全面的研究。总结了基准数据集及其特点。讨论了现有提取高判别性和抽象分布方法的优点和局限性。总结了评价AFER系统性能的方法,以及在基准数据集上实现的各种方法的比较分析。此外,本文还详细介绍了AFER领域中尚未解决的具有挑战性的问题,为AFER问题提供了一个开放式的研究领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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