面向梯度随机模板保护的直方图人脸验证

Lucas Chong Wei Jie, S. Chong
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引用次数: 0

摘要

人脸验证隐私保护方案是一种嵌入模板保护的生物识别系统,在保证数据完整性的同时对数据进行保护。提出了一种基于直方图的梯度随机模板保护方法(HOGRTP)。该方法将直方图定向梯度法作为特征提取技术,并与随机模板保护方法相结合。该方法作为一种多因素认证技术,并增加了一层数据保护,避免了由于生物特征不可替代而危及生物特征的问题。在无约束的人脸图像上,使用基准数据集Labeled face in The Wild (LFW)对HOGRTP的性能精度进行了测试。结果表明,HOGRTP比纯生物识别方案的识别率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Histogram of Oriented Gradient Random Template Protection for Face Verification
Privacy preserving scheme for face verification is a biometric system embedded with template protection to protect the data in ensuring data integrity. This paper proposes a new method called Histogram of Oriented Gradient Random Template Protection (HOGRTP). The proposed method utilizes Histogram of Oriented Gradient approach as a feature extraction technique and is combined with Random Template Protection method. The proposed method acts as a multi-factor authentication technique and adds a layer of data protection to avoid the compromising biometric issue because biometric is irreplaceable. The performance accuracy of HOGRTP is tested on the unconstrained face images using the benchmarked dataset, Labeled Face in the Wild (LFW). A promising result is obtained to prove that HOGRTP achieves a higher verification rate in percentage than the pure biometric scheme.
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