Carcagnì Pierluigi, Del Coco Marco, Leo Marco, Distante Cosimo
National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, Via della Libertà, 3, 73010 Arnesano , LE Italy.
Springerplus. 2015 Oct 26;4:645. doi: 10.1186/s40064-015-1427-3. eCollection 2015.
Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human-robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose. In particular, this paper highlights that a proper set of the HOG parameters can make this descriptor one of the most suitable to characterize facial expression peculiarities. A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline. The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out. In the second experimental phase, different publicly available facial datasets were used to test the system on images acquired in different conditions (e.g. image resolution, lighting conditions, etc.). As a final phase, a test on continuous data streams was carried out on-line in order to validate the system in real-world operating conditions that simulated a real-time human-machine interaction.
自动面部表情识别（FER）是一个越来越受关注的话题，主要是由于辅助技术应用的迅速普及，比如人机交互，其中强大的情感感知是最佳完成辅助任务的关键。本文对定向梯度直方图（HOG）描述符在FER问题中的应用进行了全面研究，强调了这种强大的技术可有效地用于此目的。特别是，本文强调，一组适当的HOG参数可使该描述符成为最适合表征面部表情特征的描述符之一。利用一个成熟的算法流程进行了一个大型实验环节，该环节可分为三个不同阶段。第一个实验阶段旨在证明HOG描述符表征面部表情特征的适用性，为此，与最常用的FER框架进行了成功的比较。在第二个实验阶段，使用不同的公开可用面部数据集对在不同条件下（如图像分辨率、光照条件等）获取的图像上的系统进行测试。作为最后一个阶段，对连续数据流进行了在线测试，以便在模拟实时人机交互的实际操作条件下验证该系统。
