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Benchmarking lightweight face architectures on specific face recognition scenarios
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2021-02-25 , DOI: 10.1007/s10462-021-09974-2
Yoanna Martínez-Díaz , Miguel Nicolás-Díaz , Heydi Méndez-Vázquez , Luis S. Luevano , Leonardo Chang , Miguel Gonzalez-Mendoza , Luis Enrique Sucar

This paper studies the impact of lightweight face models on real applications. Lightweight architectures proposed for face recognition are analyzed and evaluated on different scenarios. In particular, we evaluate the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face recognition, as well as active authentication on mobile devices. In addition, we show the lacks of using common lightweight models unchanged for specific face recognition tasks, by assessing the performance of the original lightweight versions of the lightweight face models considered in our study. We also show that the inference time on different devices and the computational requirements of the lightweight architectures allows their use on real-time applications or computationally limited platforms. In summary, this paper can serve as a baseline in order to select lightweight face architectures depending on the practical application at hand. Besides, it provides some insights about the remaining challenges and possible future research topics.



中文翻译:

在特定的人脸识别方案上对轻量级人脸架构进行基准测试

本文研究了轻型人脸模型对实际应用的影响。提出了用于面部识别的轻量级架构,并在不同情况下进行了评估。特别是,我们在五种面部识别方案上评估了五种最新轻量级体系结构的性能:基于图像和视频的面部识别,交叉因素和异构面部识别,以及在移动设备上的主动身份验证。此外,通过评估研究中考虑的轻量级面部模型的原始轻量级版本的性能,我们表明缺乏针对特定的面部识别任务使用不变的常见轻量级模型的不足。我们还表明,在不同设备上的推理时间和轻量级体系结构的计算要求允许它们在实时应用程序或计算受限的平台上使用。总而言之,本文可以作为基线,以便根据手头的实际应用选择轻量级的面部结构。此外,它还提供了有关尚存挑战和可能的未来研究主题的一些见解。

更新日期:2021-02-26
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