Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition.

本研究使用四种不同的神经网络模型，并研究了不同的人脸对齐方法对模型的影响。研究证明，适当的参数初始化和简单的预处理步骤可以提高性别识别的表现。

深度神经网络在年龄和性别分类中的理解和比较

Understanding and Comparing Deep Neural Networks for Age and Gender  Classification

This paper describes the details of Sighthound's fully automated age, gender
and emotion recognition system. The backbone of our system consists of several
deep convolutional neural networks that are not only computationally
inexpensive, but also provide state-of-the-art results on several competitive
benchmarks. To power our novel deep networks, we collected large labeled
datasets through a semi-supervised pipeline to reduce the annotation
effort/time. We tested our system on several public benchmarks and report
outstanding results. Our age, gender and emotion recognition models are
available to developers through the Sighthound Cloud API at
this https URL

本文描述了 Sighthound 的全自动年龄、性别和情感识别系统的细节，他们通过半监督流水线收集大型标签数据集来提高深度学习网络的能力，最后在几个公共基准测试中获得出色的结果，该系统现在通过 Sighthound Cloud API 向开发人员提供服务。