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首页> 外文会议>2011 International Joint Conference on Biometrics >Difficult imaging covariates or difficult subjects? - An empirical investigation
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Difficult imaging covariates or difficult subjects? - An empirical investigation

机译：成像协变量困难或受试者困难？ -实证研究

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摘要

The performance of face recognition algorithms is affected both by external factors and internal subject characteristics [1]. Reliably identifying these factors and understanding their behavior on performance can potentially serve two important goals - to predict the performance of the algorithms at novel deployment sites and to design appropriate acquisition environments at prospective sites to optimize performance. There have been a few recent efforts in this direction that focus on identifying factors that affect face recognition performance but there has been no extensive study regarding the consistency of the effects various factors have on algorithms when other covariates vary. To give an example, a smiling target image has been reported to be better than a neutral expression image, but is this true across all possible illumination conditions, head poses, gender, etc.? In this paper, we perform rigorous experiments to provide answers to such questions. Our investigation indicates that controlled lighting and smiling expression are the most favorable conditions that consistently give superior performance even when other factors are allowed to vary. We also observe that internal subject characterization using biometric menagerie-based classification shows very weak consistency when external conditions are allowed to vary.
机译：人脸识别算法的性能受外部因素和内部主体特征的影响[1]。可靠地识别这些因素并了解它们在性能上的行为可能会达到两个重要目标-预测算法在新型部署站点的性能，并在预期站点设计合适的采集环境以优化性能。在这个方向上，最近有一些努力集中在确定影响面部识别性能的因素上，但是，当其他协变量发生变化时，关于各种因素对算法的影响的一致性，还没有进行广泛的研究。举个例子，据报道微笑的目标图像要比中立的表情图像要好，但是在所有可能的照明条件，头部姿势，性别等情况下都是这样吗？在本文中，我们进行了严格的实验以提供此类问题的答案。我们的研究表明，即使允许其他因素发生变化，受控的照明和微笑的表情也是始终能够提供出色性能的最有利条件。我们还观察到，当允许外部条件变化时，使用基于生物特征识别的分类的内部主题表征显示出非常弱的一致性。

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