当前位置: X-MOL 学术Artif. Intell. Rev. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
On the frontiers of pose invariant face recognition: a review
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2019-07-27 , DOI: 10.1007/s10462-019-09742-3
Sheikh Bilal Ahmed , Syed Farooq Ali , Jameel Ahmad , Muhammad Adnan , Muhammad Moazam Fraz

Computer vision systems open a new challenge to recognize human faces under varied poses in similar capacity and capability as human-beings perform naturally. For surveillance applications, pose-invariant face recognition (PIFR) will become a major break-through by presenting the solution of this unique challenge. In recent decade, several techniques are presented to address this challenge over well-known data-sets. These efforts are divided chronologically into seven different approaches say geometric, statistical, holistic, template, supervised learning, unsupervised learning and deep learning. Among these deep learning techniques have shown more promising results and have gained attention for future research. By reviewing PIFR, it is historically divided into five eras based on 160 referred papers and their cumulative citations.

中文翻译:

在姿势不变人脸识别的前沿:回顾

计算机视觉系统开启了一个新的挑战,即识别具有与人类自然表现相似的能力和能力的各种姿势下的人脸。对于监控应用,姿态不变人脸识别 (PIFR) 将通过提出这一独特挑战的解决方案而成为一项重大突破。近几十年来,提出了几种技术来解决众所周知的数据集的这一挑战。这些努力按时间顺序分为七种不同的方法,即几何、统计、整体、模板、监督学习、无监督学习和深度学习。在这些深度学习技术中,已经显示出更有希望的结果,并受到了未来研究的关注。通过回顾 PIFR,它在历史上根据 160 篇参考论文及其累积引用被划分为五个时代。
更新日期:2019-07-27
down
wechat
bug