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Facial asymmetry-based feature extraction for different applications: a review complemented by new advances
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2021-04-10 , DOI: 10.1007/s10462-021-10001-7
Muhammad Sajid , Nouman Ali , Naeem Iqbal Ratyal , Saadat Hanif Dar , Bushra Zafar

Facial feature extraction (FFE) is considered as a challenging area for computer vision and artificial intelligence research community. There are numerous application domains of face recognition such as demographic classification and facial disease classification. In last few years, numerous feature extraction approaches are proposed. This review focuses on studies that exclusively use facial asymmetry as one of the main subject-specific facial characteristics for FFE. This paper provides a review about the related research conducted in the past two decades. First, we summarize the conventional FFE approaches and their main algorithms. Application of deep networks to facial asymmetry based FFE approaches is then presented. Multi-network deep models suitable for asymmetry-based FFE for different applications are also focused in this review. We presented the details about the publicly available face datasets, evaluation metrics, and comparison of the state-of-the-art results is also presented. The directions of asymmetry-based FFE for future research is also presented to provide an awareness about the existing and future trends. This review is presented to provide directions and ideas for future research in the field of facial asymmetry.



中文翻译:

基于面部不对称的特征提取,适用于不同应用:综述与新进展相辅相成

面部特征提取(FFE)被认为是计算机视觉和人工智能研究界的一个挑战领域。人脸识别有许多应用领域,例如人口统计分类和面部疾病分类。在最近几年中,提出了许多特征提取方法。这篇综述的重点是专门使用面部不对称作为FFE的主要受试者特定面部特征之一的研究。本文对过去二十年来进行的相关研究进行了综述。首先,我们总结了传统的FFE方法及其主要算法。然后介绍了深度网络在基于面部不对称的FFE方法中的应用。这篇综述还重点介绍了适用于不同应用的基于不对称FFE的多网络深度模型。我们介绍了有关可公开获得的人脸数据集,评估指标以及最新结果比较的详细信息。还提出了基于不对称FFE的未来研究方向,以使人们对现有和未来趋势有所了解。本文旨在为面部不对称领域的未来研究提供方向和思路。

更新日期:2021-04-11
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