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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Near Infrared and Visible Face Recognition based on Decision Fusion of LBP and DCT Features
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Near Infrared and Visible Face Recognition based on Decision Fusion of LBP and DCT Features

机译：基于LBP和DCT特征决策融合的近红外可见人脸识别

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Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.
机译：可见的面部识别系统容易受到光照，表情和姿势的影响，因此在不受限制的情况下无法获得强大的性能。同时，不依赖光的近红外人脸图像可以避免或限制可见光中人脸识别的弊端，但其主要挑战是分辨率低和信噪比（SNR）。因此，近红外和可见融合人脸识别已成为无约束人脸识别研究领域的重要方向。为了提取近红外图像与可见图像之间的区别互补特征，本文提出了一种基于DCT和LBP特征的新型近红外可见图像融合识别算法。首先，利用DCT系数的低频部分和LBP算子的分区直方图提取近红外人脸图像的有效特征。其次，提取可见光人脸图像的LBP特征以补偿近红外人脸图像缺乏的细节特征。然后，将可见光人脸图像的LBP特征，近红外人脸图像的DCT和LBP特征发送到每个分类器进行标记。最后，采用决策级融合策略获得最终的识别结果。在HITSZ Lab2可见和近红外人脸数据库上测试了可见和近红外人脸识别。实验结果表明，该方法提取了近红外和可见人脸图像的互补特征，提高了无约束人脸识别的鲁棒性。特别是对于训练样本较小的情况，该方法的识别率可以达到96.13％，比基于统计特征融合的方法的92.75％有明显提高。

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