中文摘要：

可见光图像受光源影响较大，红外图像可以独立光源，但对温度变化比较敏感，而红外与可见光融合的人脸识别方法被证明比任意单一识别更有效。为了提高人脸识别的鲁棒性，提出了一种基于决策融合的红外与可见光图像人脸识别方法，即加权求和与求最大值组合的图像决策融合方法。对红外与可将光人脸图像分别采用PCA与线性辨别分析相结合的方法进行特征提取和识别，并利用获得的识别结果与它们各自的置信度进行决策融合，确定最终的人脸识别结果。实验表明，可以有效提高人脸识别性能和对各种应用环境的适用性。

英文摘要：

The nature of the imaging environment, illumination plays an important role in the efficiency of face recognition on visible images. Infrared image is independent of the ambient illumination, but it is sensitive to temperatures. Face recognition algorithms applied to the fusion of IR and visible images consistently demonstrated better performance than when applied to either visible or IR imagery alone. An approach based on decision-level fusion of infrared and visible images for robust face recognition is presented, combinatory of linear weighted sum and biggest match score. The combination of PCA and linear discriminant analysis method was used to extract and recognize face feature. In order to achieve the final recognition result, the decision-level fusion was implemented by previous outcome of infrared and visible images recognition and their confidence measure. The experiments have shown it improves the performance and adaptability of face recognition in lots of actual application environments.