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A compressed string matching algorithm for face recognition with partial occlusion
Multimedia Systems ( IF 3.9 ) Pub Date : 2021-01-04 , DOI: 10.1007/s00530-020-00727-9
Krishnaveni Bommidi , Sridhar Sundaramurthy

There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc., cause partial occlusion which results in a significant performance drop of the face recognition system. In this paper, a novel compressed string matching algorithm based on run-length encoding (CSM-RL) is proposed to solve the partial occlusion problem. In this, the face image is represented by a string sequence that is then compressed using run-length encoding. The proposed CSM-RL algorithm performs string matching between query face and gallery face string sequences by computing the edit distance between string sequences, finally, classifies query face based on the minimum edit distance. The proposed method does not require a classifier and has less time complexity, thus it is more suitable for real-world face recognition applications. The proposed method performs better than the state-of-the-art methods even limited sample images per person are available in the gallery. Extensive experimental results on benchmark face datasets such as AR and Extended Yale-B prove that the proposed algorithm exhibits significant performance improvement both in terms of speed and recognition accuracy for the recognition of partially occluded faces.

中文翻译:

一种用于部分遮挡人脸识别的压缩字符串匹配算法

部分遮挡的人脸识别研究较少受到关注。口罩、墨镜、帽子等面部配饰会造成局部遮挡,导致人脸识别系统性能显着下降。在本文中,提出了一种新的基于游程长度编码(CSM-RL)的压缩字符串匹配算法来解决部分遮挡问题。在这种情况下,面部图像由一个字符串序列表示,然后使用运行长度编码进行压缩。所提出的CSM-RL算法通过计算字符串序列之间的编辑距离对查询人脸和图库人脸字符串序列进行字符串匹配,最后根据最小编辑距离对查询人脸进行分类。所提出的方法不需要分类器并且时间复杂度较低,因此它更适合现实世界的人脸识别应用。所提出的方法比最先进的方法表现更好,即使图库中每个人的样本图像有限。在 AR 和 Extended Yale-B 等基准人脸数据集上的大量实验结果证明,所提出的算法在识别部分遮挡人脸的速度和识别精度方面均表现出显着的性能提升。
更新日期:2021-01-04
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