Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures – RECOD Selfie data set – that hopefully will support future research in this scenario.

中文翻译：

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面向移动设备的低内存占用的两层面部验证

近年来，移动设备的普及和可负担性大大提高。由于它们无处不在，因此这些设备现在携带各种个人数据，这些数据只能由其所有者访问。尽管基于知识的程序仍然是确保所有者身份安全的主要方法，但近来生物特征已被用于更安全，更轻松的身份验证。在这项工作中，作者提出了针对移动环境优化的面部验证方法。它由两层程序组成，该程序将手工制作的功能与卷积神经网络（CNN）相结合，以验证照片中描绘的人是否对应于设备所有者。为了训练CNN进行验证任务，作者提出了一种混合图像输入，允许网络处理一对面部图像的编码信息。拟议的实验表明，该解决方案优于最新的人脸验证方法，在最近的智能手机型号中处理图像时，其速度提高了4倍。此外，作者表明，可以将两层过程与现有的面部验证CNN结合使用，从而提高其准确性和效率。他们还提供了新的自拍照图片数据集-RECOD Selfie数据集-有望在这种情况下为将来的研究提供支持。作者表明，可以将两层过程与现有的面部验证CNN结合使用，从而提高其准确性和效率。他们还提供了新的自拍照图片数据集-RECOD Selfie数据集-有望在这种情况下为将来的研究提供支持。作者表明，可以将两层过程与现有的面部验证CNN结合使用，从而提高其准确性和效率。他们还提供了新的自拍照图片数据集-RECOD Selfie数据集-有望在这种情况下为将来的研究提供支持。