Mobility implies a great variability of capturing conditions, which is not easy to control and directly affects to face detection and the extraction of facial features. Deep learning solutions seem to be the most interesting choice for automatic face recognition, but they are highly dependent on the model generated during the training stage. In addition, the size of the models makes it difficult for their integration into applications oriented to mobile devices, particularly when the model must be embedded. In this work, a small-size deep-learning model was trained for face recognition on low capacity devices and evaluated in terms of accuracy, size and timings to provide quantitative data. This evaluation is aimed to cover as many scenarios as possible, so different databases were employed, including public and private datasets specifically oriented to recreate the complexity of mobile scenarios. Also, publicly available models and traditional approaches were included in the evaluation to carry out a fair comparison. Moreover, given the relevance of template matching and face detection stages, the assessment is complemented with different classifiers and detectors. Finally, a JAVA-Android implementation of the system was developed and evaluated to obtain performance data of the whole system integrated on a mobile phone.

中文翻译：

---

深度学习在移动设备上进行人脸识别

移动性意味着捕获条件的可变性很大，这不容易控制，直接影响面部检测和面部特征的提取。深度学习解决方案似乎是自动人脸识别最有趣的选择，但它们高度依赖于训练阶段生成的模型。此外，模型的大小使它们难以集成到面向移动设备的应用程序中，特别是当必须嵌入模型时。在这项工作中，为在低容量设备上进行人脸识别训练了一种小型深度学习模型，并在准确性，大小和时序方面进行了评估，以提供定量数据。此评估旨在涵盖尽可能多的场景，因此采用了不同的数据库，包括专门用于重现移动场景复杂性的公共和私有数据集。此外，评估中包括了公开可用的模型和传统方法，以进行公平的比较。此外，考虑到模板匹配和面部检测阶段的相关性，评估还辅以不同的分类器和检测器。最后，开发并评估了该系统的JAVA-Android实现，以获取集成在手机上的整个系统的性能数据。该评估辅以不同的分类器和检测器。最后，开发并评估了该系统的JAVA-Android实现，以获取集成在手机上的整个系统的性能数据。该评估辅以不同的分类器和检测器。最后，开发并评估了该系统的JAVA-Android实现，以获取集成在手机上的整个系统的性能数据。