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Age-variation face recognition based on bayes inference

机译:基于贝叶斯推理的年龄变异面部识别

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Studies have discovered that face recognition will benefit from age information. However, since the age estimation is unstable in practice, it is still an open question how to improve face recognition using age estimation algorithms. This paper presents to improve the performance of face recognition by automatic age estimation. The main contribution is a new age-variational face recognition algorithm based on Bayesian framework (FRAB). By introducing the age estimation result as a prior, the recognition problem is divided into several age-specific sub-problems. As a result, the proposed algorithm leads to two algorithms according to how the age is given. The first one is FRAB-AE, which introduces age estimation result as the age prior. The second one is FRAB-GT, which considers that the ground truth of age information is given. Experimental results are conducted on FG-NET dataset to evaluate the performance of the proposed framework. It shows that the proposed algorithms is able to make use of age priors to improve the face recognition.
机译:研究发现,人脸识别将受益于年龄信息。然而,由于年龄估计在实践中不稳定,因此仍然是如何使用年龄估计算法改善人脸识别的开放问题。本文提出了通过自动年龄估计来提高人脸识别的性能。主要贡献是基于贝叶斯框架(FRAB)的新年龄变分识别算法。通过将年龄估计结果作为先前引入,识别问题分为几个特定年龄的子问题。结果,所提出的算法根据年龄的给出方式导致两种算法。第一个是FRAB-AE,它将年龄估计结果作为年龄提交。第二个是FRAB-GT,这考虑到年龄信息的地面真理。实验结果是在FG-NET数据集上进行的,以评估所提出的框架的性能。它表明,所提出的算法能够利用年龄前沿来改善面部识别。

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