Efficient Face Verification Algorithm with Attention Mechanism
首发时间:2019-03-15
Abstract:Face verification is applied to our life, which benefits from the high-accuracy of the algorithm based on CNN. However,the performance of face verification is still poor on mobile device since limited computation resources. In this paper, we present a class of extremely efficient algorithm with attention mechanism embedded, the algorithm of 20MB size achieves 96.37% face verification TAR(FAR1e-6) on MegaFace Challenge, which is even comparable to hundrads MB size. We compare our algorithm with similar small size models like MobiFace, MobileFaceNet, Goole-FaceNet, the experimental results show the efficent of our algorithm.
keywords: Pattern Recognition Face Verification Attention Mechanism Depth wise Separable Convolution
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嵌入注意力机制的高效人脸识别算法
摘要:基于深度学习的人脸识别算法在识别精度上不断提升,促使人脸识别功能从实验进入实用。然而,人脸识别仍受限于移动端匮乏的计算资源,表现上差强人意。本文提出了一种嵌入注意力机制的人脸识别算法,可以控制算法参数量在20MB的情况下,达到之前上百MB算法的效果,在MegaFace数据集上实现误检率为百万分之一,识别率为96.37%。在和MobiFace、MobileFaceNet、Google-FaceNet等参数量相近的算法进行了对比,验证了本文提出算法的高效性。
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