Deep Learning (13) - Face Recognition

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本章讨论的是目标检测的一个特殊用例——人脸识别。所谓人脸识别，就是输入一张照片，或者现场采集头像照片，并匹配数据库中的数据，来翻译成身份信息。通常一个人脸识别算法可以分作两步：

  • Face verification
  • Face recognition

前者输入照片和ID信息，判断是否匹配。后者输入照片信息，与数据库中已有的照片集进行匹配。当前者算法精确度足够高时，就可以应用到后者中。

One Shot Learning

Face recognition首要解决的就是One Shot Learning问题。例如公司的门禁系统，通常每个员工只上传一张个人照片，那么这个系统就要能够正确识别该员工。即便所有的员工都上传了照片，其实这个样本集的数量级仍然不会很高。怎么在一个小样本集上有效训练来达到比较好的效果，这就是One Shot Learning需要解决的问题。后面提到的Siamese网络就是One Shot Learning的一种解决方案。

Siamese Network

Siamese就是下图这样的一个网络。它包含2个或者更多个完全一样的网络分支。每个分支将输入数据映射成最终的activation向量输出。然后通过比较这两个向量的相似度，来度量两幅图片的相似度。


其中两路输出的difference用下式来表明：

如果输出层，采用sigmoid激活函数，则预测输出为：

上面是训练好所有权重后，如何使用一个Siamese网络来得到图片异同的预测。那么如何训练一个Siamese网络呢？要训练一个网络，首先我们需要模型的损失函数J，其次我们需要满足训练的样本集。

Triplet Loss

要训练一个Siamese网络，它的输入样本可以这样选择


一共3张图片，分成两组，训练的目标是左边一组输出的difference函数要小于右边一组输出的difference函数。考虑随机噪声，再添加一些margin，可以得到损失函数如下：

  • 就是margin
  • A代表Anchor，P代表Positive，N代表Negative
  • m是mini-batch中的样本数

如果有1k个人的10k张图片，可以生成很多个这样APN的样本组。生成时，最好不要采用随机算法，因为如果是随机生成APN的话，导致AN很有可能图片本身差别就很大，所以就很容易满足，这样的样本就是无效样本。在生成样本时，尤其是挑选AN对时，尽量挑选比较相似的，以进行有效训练。
通过对该损失函数J进行前向传播和反向传播，来运行Gradient descent算法，迭代得到最终Siamese网络的模型参数。

参考文献

  • Siamese Network: Taigman et. al., 2014, DeepFace closing the gap to human level performance.
  • Triplet Loss: Schroff et. al., 2015, FaceNet: A unified embedding for face recognition and clustering.
  • Siamese Network & Triplet Loss
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