The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face recognition problems. Any language in the world has a separate set of words and grammatical rules when addressing people of different ages. The decision associated with its usage, relies on our ability to demarcate these individual characteristics like gender and age from the facial appearances at one glance. With the rapid usage of Artificial Intelligence (AI) based systems in different fields, we expect that such decision making capability of these systems match as much as to the human capability. To this end, in this work, we have designed a deep learning based model, called GRA_Net (Gated Residual Attention Network), for the prediction of age and gender from the facial images. This is a modified and improved version of Residual Attention Network where we have included the concept of Gate in the architecture. Gender identification is a binary classification problem whereas prediction of age is a regression problem. We have decomposed this regression problem into a combination of classification and regression problems for achieving better accuracy. Experiments have been done on five publicly available standard datasets namely FG-Net, Wikipedia, AFAD, UTKFAce and AdienceDB. Obtained results have proven its effectiveness for both age and gender classification, thus making it a proper candidate for the same against any other state-of-the-art methods.

中文翻译：

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GRA_Net：根据面部图像对年龄和性别进行分类的深度学习模型


许多研究人员已经解决了性别和年龄识别问题，然而，与人脸识别的其他相关问题相比，对它的关注要少得多。与其他人脸识别问题相比，该领域取得的成功并没有太大的进步。世界上任何语言在称呼不同年龄段的人时都有一套单独的词语和语法规则。与其使用相关的决定取决于我们一眼就能从面部外观中区分出性别和年龄等个人特征的能力。随着基于人工智能（AI）的系统在不同领域的快速使用，我们期望这些系统的决策能力能够与人类的能力相匹配。为此，在这项工作中，我们设计了一种基于深度学习的模型，称为 GRA_Net（门控残差注意力网络），用于从面部图像预测年龄和性别。这是残差注意力网络的修改和改进版本，我们在架构中包含了门的概念。性别识别是一个二元分类问题，而年龄预测是一个回归问题。我们已将此回归问题分解为分类和回归问题的组合，以实现更好的准确性。在五个公开可用的标准数据集（即 FG-Net、Wikipedia、AFAD、UTKFAce 和 AdienceDB）上进行了实验。获得的结果证明了其对于年龄和性别分类的有效性，从而使其成为与任何其他最先进方法相比的合适候选者。