We propose a method designed to push the frontiers of unconstrained face recognition in the wild with an emphasis on extreme out-of-plane pose variations. Existing methods either expect a single model to learn pose invariance by training on massive amounts of data or else normalize images by aligning faces to a single frontal pose. Contrary to these, our method is designed to explicitly tackle pose variations. Our proposed Pose-Aware Models (PAM) process a face image using several pose-specific, deep convolutional neural networks (CNN). 3D rendering is used to synthesize multiple face poses from input images to both train these models and to provide additional robustness to pose variations at test time. Our paper presents an extensive analysis of the IARPA Janus Benchmark A (IJB-A), evaluating the effects that landmark detection accuracy, CNN layer selection, and pose model selection all have on the performance of the recognition pipeline. It further provides comparative evaluations on IJB-A and the PIPA dataset. These tests show that our approach outperforms existing methods, even surprisingly matching the accuracy of methods that were specifically fine-tuned to the target dataset. Parts of this work previously appeared in [1] and [2] .

中文翻译：

---

学习姿势感知模型以实现野外姿势不变人脸识别


我们提出了一种方法，旨在推动野外无约束人脸识别的前沿，重点关注极端的平面外姿势变化。现有的方法要么期望单个模型通过大量数据的训练来学习姿势不变性，要么通过将面部与单个正面姿势对齐来标准化图像。与这些相反，我们的方法旨在明确处理姿势变化。我们提出的姿势感知模型 (PAM) 使用多个特定姿势的深度卷积神经网络 (CNN) 处理面部图像。 3D 渲染用于从输入图像合成多个面部姿势，以训练这些模型并为测试时的姿势变化提供额外的鲁棒性。我们的论文对 IARPA Janus Benchmark A (IJB-A) 进行了广泛的分析，评估了地标检测精度、CNN 层选择和姿势模型选择对识别管道性能的影响。它还进一步提供了 IJB-A 和 PIPA 数据集的比较评估。这些测试表明，我们的方法优于现有方法，甚至令人惊讶地与专门针对目标数据集进行微调的方法的准确性相匹配。这项工作的部分内容先前出现在 [1] 和 [2] 中。