Light-CNN与FaceNet人脸识别与维护方法的对比分析

Huang Yea-Shuan, Mahmood Alhlffee
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引用次数: 0

摘要

在合成正面视图图像的同时保持身份是建立“生成识别”框架的最关键步骤。为此,本文调查、测试和比较了两种深度学习架构:Light-CNN和FaceNet的性能。Light-CNN用于学习人脸验证任务的鲁棒特征,该特征比许多传统深度学习模型产生更高的人脸识别精度。另一方面,FaceNet是一个将人脸图像映射到紧凑的欧几里得空间的模型,其中距离直接表示人脸相似性的度量。在我们的比较中,我们使用TP-GAN模型执行几个预处理阶段。然后使用Light-CNN和FaceNet分别作为256维和128维表示从合成的人脸图像中提取人脸特征。我们评估了Light-CNN和FaceNet架构在Multi-PIE和FEI数据集上的精度性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of the Light-CNN and FaceNet methods for identifying and maintaining human faces
Maintaining the identity while synthesizing the frontal view image is the most critical step in developing a "recognition via generation" framework. To this end, this paper investigates, tests and compares the performance of two deep learning architectures: Light-CNN and FaceNet. The Light-CNN is used to learn a robust feature for face verification tasks that produces a high-level facial identity accuracy over many traditional deep learning models. FaceNet, on the other hand, is a model to maps face images into a compact Euclidean space where distances directly represent a measure of face similarity. In our comparison, we use the TP-GAN model to perform several pre-processing stages. The face features are then extracted from the synthesized face images using Light-CNN and FaceNet as 256- and 128-dimensional representations, respectively. We evaluate the accuracy performances of Light-CNN and FaceNet architectures on Multi-PIE and FEI datasets.
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