为了利用多个数据集上所有的信息，如人脸框，人脸关键点，姿态，性别，年龄，笑容，和ID信息，多个子网络可以关于任务相关的数据集进行训练，然后将参数进行共享，因为没有一个单一的数据集包含所有人脸分析任务所需的标注信息。通过这种方法，我们可以用参数共享的方式来自适应整个领域，而不是去拟合具体任务领域。在测试的时候，这些子网络融合到一个单一的all in one face中。表5列出了基于不同数据集下训练all in one face。
具体的loss函数用来端到端的训练该网络。all in one face网络输出结果在图9。
基于MTL的DCNN同样可以用来决定多个人脸属性。Depghan提出深度年龄，性别和表情识别（deep age， gender， and emotion recognition，dager）[111]，基于DCNN网络去识别年龄，性别，表情。类似all in one face[2]，它基于不同的任务采用不同的数据集去训练该DCNN。He[112]通过训练一个网络去联合的做人脸检测和人脸属性分析。不同于其他MTL方法，他们使用整个图片作为网络的输入，而不只是人脸本身的区域。一个基于faster rcnn的方法可以用来一起检测人脸，表6总结了一些近期基于MTL方法的人脸分析任务
我们简短的讨论了对于一个自动人脸验证和失败系统的每个组件上的设计思路。包括：
人脸检测：相对通用目标检测，人脸检测是一个更具有挑战的任务，因为涉及到人脸的多种变化，这些变化包含光照的，人脸表情的，人脸角度的，遮挡等等。其他因素如模糊和低分辨率一样增大了该任务的难度；
关键点检测：大多数数据集包含几千张图片，一个很大的标注和无约束数据集会使得人脸对齐系统具有更强的鲁棒性来应对其中的挑战，如极端的姿态，低光照和小的，模糊的人脸图像。研究者们假设更深的CNN能够抓取更鲁棒的信息；然而目前为止，仍然未研究出哪些层能够准确的提取局部特征来做人脸关键点检测。
人脸验证/识别：对于人脸识别和验证而言，性能可以通过学习一个判别性距离度量来提升。由于受显卡的内存限制，如何选择信息对或三元组并使用大规模数据集上的在线方法（例如，随机梯度下降）端到端地训练网络仍然是一个悬而未决的问题。要解决的另一个具有挑战性的问题是在深度网络中加入全动态视频处理，以实现基于视频的人脸分析。
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