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Learning a Metric Embedding for Face Recognition using the Multibatch Method

Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua
Orcam Ltd., Jerusalem, Israel
arXiv:1605.07270 [cs.CV], (24 May 2016)
@article{tadmor2016learning,

   title={Learning a Metric Embedding for Face Recognition using the Multibatch Method},

   author={Tadmor, Oren and Wexler, Yonatan and Rosenwein, Tal and Shalev-Shwartz, Shai and Shashua, Amnon},

   year={2016},

   month={may},

   archivePrefix={"arXiv"},

   primaryClass={cs.CV}

}

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This work is motivated by the engineering task of achieving a near state-of-the-art face recognition on a minimal computing budget running on an embedded system. Our main technical contribution centers around a novel training method, called Multibatch, for similarity learning, i.e., for the task of generating an invariant "face signature" through training pairs of "same" and "not-same" face images. The Multibatch method first generates signatures for a mini-batch of $k$ face images and then constructs an unbiased estimate of the full gradient by relying on all $k^2-k$ pairs from the mini-batch. We prove that the variance of the Multibatch estimator is bounded by $O(1/k^2)$, under some mild conditions. In contrast, the standard gradient estimator that relies on random $k/2$ pairs has a variance of order $1/k$. The smaller variance of the Multibatch estimator significantly speeds up the convergence rate of stochastic gradient descent. Using the Multibatch method we train a deep convolutional neural network that achieves an accuracy of $98.2%$ on the LFW benchmark, while its prediction runtime takes only $30$msec on a single ARM Cortex A9 core. Furthermore, the entire training process took only 12 hours on a single Titan X GPU.
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