Department of Human Cognition, Perception and Action, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany.
PLoS One. 2012;7(3):e32321. doi: 10.1371/journal.pone.0032321. Epub 2012 Mar 15.
The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision) to investigate the processing of a wider range of natural facial expressions.
沟通能力是人类生活的核心方面之一。为此,我们不仅使用言语,还使用非常复杂的非言语信号。在后者中,面部表情属于最重要的信息渠道。尽管我们在日常生活中使用的面部表情种类繁多,但对面部表情的研究迄今为止主要集中在情感方面。因此,研究界可用的大多数面部表情数据库也只包括情感表情,而忽略了尚未充分研究的会话表情方面。为了填补这一空白,我们提出了 MPI 面部表情数据库,其中包含大量自然的情感和会话表情。该数据库包含 19 名德国参与者表演的 55 种不同的面部表情。表情是通过一种方法表演协议来激发的,该协议保证了定义明确和自然的面部表情。该方法表演协议基于日常场景,用于为每个表情定义必要的上下文信息。所有面部表情均以三种重复,两种强度以及三种不同的相机角度提供。提供了详细的帧注释,从中创建了数据库的动态和静态版本。除了详细描述数据库外,我们还介绍了两项条件实验的结果,这些实验可用于验证上下文场景以及视频序列的自然性和可识别性。我们的结果提供了明确的证据,表明仅从视觉信息就可以对面部表情进行惊人的识别。MPI 面部表情数据库将使来自不同研究领域的研究人员(包括感知和认知科学,情感计算以及计算机视觉)能够研究更广泛的自然面部表情的处理方式。