This work introduces a high-efficiency approach for face recognition applications based on features using a recent algorithm called Floor of Log (FoL). The advantage of this method is the reduction of storage and energy, maintaining accuracy. K-Nearest Neighbors and Support Vector Machine algorithm was applied to learn the better parameter of the FoL algorithm using cross-validation. Accuracy and the size after the compression process were adopted to evaluate the proposed algorithm. The FoL was tested in CelebA, Extended YaleB, AR, and LFW face datasets obtaining the same or better results when compared with the approach using the same classifiers with uncompressed features, but with a reduction of 86 to 91% compared to the original data size. The proposed method of this work presents a robust and straightforward algorithm of compression of features for face recognition applications. The FoL is a new supervised compression algorithm that can be adapted to achieve great results and integrated with edge computing systems.

这项工作为基于特征的人脸识别应用程序引入了一种高效的方法，该方法使用了最新的称为地板底数（FoL）的算法。这种方法的优点是减少了存储和能源，保持了准确性。应用K最近邻和支持向量机算法，通过交叉验证来学习FoL算法的更好参数。通过压缩过程的精度和大小来评估该算法。与使用具有未压缩特征的相同分类器的方法相比，FolL在CelebA，Extended YaleB，AR和LFW人脸数据集中进行了测试，获得了相同或更好的结果，但与原始数据大小相比减少了86％至91％ 。这项工作的拟议方法提出了一种用于面部识别应用的功能强大而直接的算法。FoL是一种新的监督压缩算法，可以对其进行调整以实现出色的结果，并且可以与边缘计算系统集成。