随着生物特征识别技术的广泛应用,其安全性方面的缺陷也逐渐暴露出来。密码技术与生物特征识别技术相结合的生物特征加密技术,就是为了弥补生物特征识别在安全方面的不足而产生的。在研究已有人脸生物特征识别技术的基础上,提出一种兼具安全性及容错能力的人脸生物特征加密算法:模糊循环随机映射(Fuzzy Cyclic Random Mapping,FCRM)。在每次循环中,加密模型使用前一次循环的密钥作为随机种子生成映射矩阵,对用户的人脸特征进行映射,形成一个循环的随机映射过程。加密过程中,还使用了容错技术来减少合法用户人脸图像和特征的随机噪声对识别率的影响,而循环的映射过程能够在不减少认证准确率的前提下,阻止非法用户通过认证。
In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks.
