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首页> 外文期刊>IEEE transactions on information forensics and security >Face Recognition Using Sparse Fingerprint Classification Algorithm
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Face Recognition Using Sparse Fingerprint Classification Algorithm

机译：稀疏指纹分类算法的人脸识别

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Unconstrained face recognition is still an open problem as the state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. This paper addresses this problem by proposing a new approach called sparse fingerprint classification algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject’s face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. The results demonstrate that when the size of the data set is small or medium (e.g., the number of subjects is not greater than one hundred), SFCA is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size, and distance from the camera than other current state-of-the-art algorithms.
机译：无约束的人脸识别仍然是一个未解决的问题，因为最新的算法尚未在现实​​环境中达到很高的识别性能。本文通过提出一种称为稀疏指纹分类算法（SFCA）的新方法来解决此问题。在训练阶段，对于每个已注册的主题，都会从每个主题的面部图像中提取一系列网格，以构建代表性词典。在测试阶段，从查询图像中提取网格，并使用字典将每个面片转换为二进制稀疏表示，从而创建面部指纹。二进制系数为其对应的类别投票，而最大投票类别决定查询图像的身份。在七个广泛使用的人脸数据库上进行了实验。结果表明，当数据集的大小较小或中等时（例如，受试者的数量不超过一百），SFCA能够处理环境光照，姿势，表情，遮挡的较大程度的可变性，面部大小和距相机的距离，而不是其他当前最新算法。

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