Covariates are factors that have a debilitating influence on face
verification performance. In this paper, we comprehensively study two covariate
related problems for unconstrained face verification: first, how covariates
affect the performance of deep neural networks on the large-scale unconstrained
face verification problem; second, how to utilize covariates to improve
verification performance. To study the first problem, we implement five
state-of-the-art deep convolutional networks (DCNNs) for face verification and
evaluate them on three challenging covariates datasets. In total, seven
covariates are considered: pose (yaw and roll), age, facial hair, gender,
indoor/outdoor, occlusion (nose and mouth visibility, eyes visibility, and
forehead visibility), and skin tone. These covariates cover both intrinsic
subject-specific characteristics and extrinsic factors of faces. Some of the
results confirm and extend the findings of previous studies, others are new
findings that were rarely mentioned previously or did not show consistent
trends. For the second problem, we demonstrate that with the assistance of
gender information, the quality of a pre-curated noisy large-scale face dataset
for face recognition can be further improved. After retraining the face
recognition model using the curated data, performance improvement is observed
at low False Acceptance Rates (FARs) (FAR=$10^{-5}$, $10^{-6}$, $10^{-7}$).

该文章主要探讨不受限制的人脸验证中的一个问题：协变量如何影响深度卷积神经网络在大规模人脸验证问题上的性能，并介绍了如何利用性别信息来改进预先筛选的大规模人脸数据集的性能.

无约束人脸验证中协变量影响的实验评估

An Experimental Evaluation of Covariates Effects on Unconstrained Face  Verification

In this work, we present an unconstrained face verification algorithm and
evaluate it on the recently released IJB-A dataset that aims to push the
boundaries of face verification methods. The proposed algorithm couples a deep
CNN-based approach with a low-dimensional discriminative embedding learnt using
triplet similarity constraints in a large margin fashion. Aside from yielding
performance improvement, this embedding provides significant advantages in
terms of memory and post-processing operations like hashing and visualization.
Experiments on the IJB-A dataset show that the proposed algorithm outperforms
state of the art methods in verification and identification metrics, while
requiring less training time.

提出了一种结合深度卷积神经网络和三元组相似性约束的低维判别嵌入的无约束人脸验证算法，在 IJB-A 数据集上显示出超越现有方法的性能提升和内存优化的优点。