自然情感表达识别：音频、视觉与自发表现的综述

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"A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions" 本文是一篇关于情感识别方法的综合研究论文，由Zhihong Zeng、Maja Pantic、Glenn I. Roisman和Thomas S. Huang等作者撰写。他们都是在计算机科学领域具有影响力的专家。该文探讨了自动分析人类情感行为的重要性，特别是在心理学、计算机科学、语言学、神经科学等相关学科中的关注点。目前的研究主要集中在典型情绪的刻意表现和夸大表达上，但这种故意的行为在视觉外观、音频特征和时间上与自然发生的行为存在差异。文章指出，为了弥补这一差距，研究者们已经开始致力于开发能够处理自然发生的人类情感行为的算法。这包括但不限于面部表情、头部运动和身体手势等多种线索的视觉融合。同时，越来越多的工作集中在多模态融合技术上，用于人类情感分析，如音频视觉融合、语言和副语言融合。具体来说，音频情感识别方法着重于通过语音特征，如音调、节奏和强度，来判断说话者的情绪状态。视觉情感识别则主要依赖于面部表情分析，包括微表情、眼睛运动、嘴唇形状等。而自发性表达的识别则更具挑战性，因为它涉及到捕捉并解析那些非刻意、无意识的情感流露。此外，论文还讨论了多模态融合技术，它将不同的感官输入（如视觉和听觉）结合起来，以提高情感识别的准确性和鲁棒性。例如，音频和视觉信息的融合可以更全面地理解语境，因为声音和面部表情常常能互相补充，提供情绪的更多信息。语言和副语言的融合则考虑到了语调、语速和停顿等因素，这些都是情感表达的重要组成部分。 "A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions" 提供了一个详尽的框架，概述了情感识别领域的最新进展。它强调了自然情感表达处理和多模态融合在推动情感智能和人机交互方面的重要作用，并为未来的研究方向提供了指导。这篇综述性文章对于了解情感识别领域的现状以及其潜在应用，如情感计算、人机交互、心理健康监测等领域具有极高的参考价值。

decision making. As indicated by Cohn [26], the most

commonly used sign judgment method for the manual

labeling of facial behavior is the Facial Action Coding

System (FACS) proposed by Ekman et al. [43]. FACS is a

comprehensive and anatomically based system that is used

to measure all visually discernible facial movements in

terms of atomic facial actions called Action Units (AUs). As

AUs are independent of interpretation, they can be used for

any high-level decision-making process, including the

recognition of basic emotions according to Emotional FACS

(EMFACS) rules

, the recognition of various affective states

accord ing to the FACS Affect Interp retation Databa se

(FACSAID)

introduced by Ekman et al. [43], and the

recognition of other complex psychological states such as

depression [47] or pain [144]. AUs of the FACS are very

suitable to use in studies on human naturalistic facial

behavior, as the thousands of anatomically possible facial

expressions (independent of their high-level interpretation)

can be described as combinations of 27 basic AUs and a

number of AU descriptors. It is not surprising, therefore,

that an increasing number of studies on human sponta-

neous facial behavior are based on automatic AU recogni-

tion (e.g., [10], [27], [135], [87], and [134]).

Speech is another important communicative modality in

human-human interaction. Speech conveys affective infor-

mation through explicit (linguistic) and implicit (paralin-

guistic) messages that reflect the way that the words are

spoken. As the linguistic content is concerned, some

inform ation about the speak er’s affective state can be

inferred directly from the surface features of words, which

were summarized in some affective word dictionaries and

lexical affi nity [110] , [142], and the rest of affective

information lies below the text surface and can only be

detected when the semantic context (e.g., discourse infor-

mation) is taken into account. However, findings in basic

research [1], [55] indicate that linguistic messages are rather

unreliable means of analyzing human (affective) behavior,

and it is very difficult to anticipate a person’s word choice

and the associated intent in affective expressions. In

addition, the association between linguistic content and

emotion is language dependent, and generalizing from one

language to another is very difficult to achieve.

When it comes to implicit paralinguistic messages that

convey affective information, basic researchers have not

identified an optimal set of voice cu es that reliabl y

discriminate among emotions. Nonetheless, listeners seem

to be accurate in decoding some basic emotions from

prosody [70] and some nonbasic affective states such as

distress, anxiety, boredom, and sexual interest from

nonlinguistic vocalizations like laughs, cries, sighs, and

yawns [113]. Cowie et al. [31] provided a comprehensive

summary of qualitative acoustic correlations for proto-

typical emotions.

In summary, a large number of studies in psychology

and linguistics confirm the correlation between some

affective displays (especially prototypical emotions) and

specific audio and visual signals (e.g., [1], [47], and [113]).

The human judgment agreement is typically higher for

facial expression modality than for vocal expression

modality. However, the amount of the agreement drops

considerably when the stimuli are spontaneously displayed

expressions of affective behavior rather than posed ex-

aggerated displays. In addition, facial expression and the

vocal expression of emotion are often studied separately.

This precludes finding evidence of the temporal correlation

between them. On the other hand, a growing body of

research in cognitive sciences argues that the dynamics of

human behavior are crucial for its interpretation (e.g., [47],

[113], [116], and [117]). For example, it has been shown that

temporal dynamics of facial behavior represent a critical

factor for distinction between spontaneous and posed facial

behavior (e.g., [28], [47], [135], and [134]) and for categor-

ization of complex behaviors like pain, shame, and

amusement (e.g., [47], [144], [4], and [87]). Based on these

findings, we may expect that the temporal dynamics of each

modality (facial and vocal) and the temporal correlations

between the two modalities play an important role in the

interpretation of human naturalistic audiovisual affective

behavior. However, these are virtually unexplored areas of

research.

Another largely unexplored area of research is that of

context dependency. The interpretation of human behavior-

al signals is context dependent. For example, a smile can be

a display of politeness, irony, joy, or greeting. To interpret a

behavioral signal, it is important to know the context in

which this sign al has been displayed, i.e., where the

expresser is (e.g., inside, on the street, or in the car), what

the expresser’s current task is, who the receiver is, and who

the expresser is [113].

3THE STATE OF THE ART

Rather than providing exhaustive coverage of all past

efforts in the field of automatic recognition of human affect,

we focus here on the efforts recently proposed in the

literature that have not been reviewed elsewhere, that

represent multimodal approaches to the problem of human

affect recognition, that address the problem of the auto-

matic analysis of spontaneous affective behavior, or that

represent ex emplary a pproaches to treating a specific

problem relevant for achieving a better human affect

sensing technology. Due to limitations on space and our

knowledge, we sincerely apologize to those authors whose

work is not included in this paper. For exhaustive surveys

of the past efforts in the field, readers are referred to the

following articles:

. Overviews of early work on facial expression

analysis: [115], [101], and [49].

. Surveys of techniques for automatic facial muscle

action recognition and facial expression analysis:

[130] and [98].

. Overviews of multimodal affect recognition meth-

ods: [31], [102], [105], [121], [68], and [152] (this is a

short preliminary version of the survey presented in

this current paper).

In this section, we first offer an overview of the existing

databases of audio and/or visual recordings of human

affective displays, which provide the basis of automatic

42 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 1, JANUARY 2009

2. http://face-and-emotion.com/dataface/general/homepage.jsp.

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