In this paper, we propose a recursive learning formulation for online heterogeneous face recognition (HFR). The main task is to compare between images which are acquired from different sensing spectrums for identity recognition. Using an extreme learning machine, the proposed recursive formulation seeks a direct optimization to the classification error goal where the solution converges exactly to the batch mode solution. Due to the nonlinear nature of the classification error objective function, formulation of a recursive solution that converges is an important and nontrivial task. Based on this recursive formulation, an online HFR system is designed. The system is evaluated using two challenging heterogeneous face databases with images captured under visible, near infrared and infrared spectrums. The proposed system shows promising performance which is comparable with that of competing state-of-the-arts.

中文翻译：

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基于总错误率最小化的在线异构人脸识别

在本文中，我们提出了一种用于在线异构人脸识别（HFR）的递归学习公式。主要任务是比较从不同传感光谱获取的图像以进行身份​​识别。使用极限学习机，建议的递归公式寻求对分类错误目标的直接优化，其中解决方案完全收敛到批处理模式解决方案。由于分类误差目标函数的非线性性质，收敛的递归解决方案的公式化是一项重要且重要的任务。基于这个递归公式，设计了一个在线 HFR 系统。该系统使用两个具有挑战性的异构人脸数据库进行评估，其中包含在可见光、近红外和红外光谱下捕获的图像。