Abstract: According to the information complementarity for visible image and infrared image, this paper analyzes normalization method and fusion algorithm for fusion recognition at decision level, proposes a Fisher projection fusion algorithm based on statistics. This algorithm employs Fisher linear discriminate criterion to find out the optimal projection in score space that can best separate projected samples from different classes. Application of multi-spectral face fusion recognition results show that this algorithm has higher recognition effect compared with other fusion algorithm.

Key words: infrared image, visible image, Fisher linear discrimination, face recognition, fusion

摘要： 根据可见光图像与红外图像的信息互补性，分析在决策层融合识别中的归一化法和融合算法，提出一种基于统计的Fisher投影融合法，利用Fisher线性判别准则在二维分数空间寻找最优投影方向，使不同类样本投影后能最佳分离。在多光谱人脸融合识别中的应用结果表明，与其他融合算法相比，该算法具有更好的识别效果。

关键词: 红外图像, 可见光图像, Fisher线性判别, 人脸识别, 融合

CLC Number: 

• TP391.41