Facial expression has been a focus of emotion research for over a hundred years. In recent decades observations of facial expressions have yielded critical and dramatic insights about the etiology of psychopathology, and have proven capable of predicting treatment outcomes (see Ekman &Rosenberg, 2005). Despite these original striking findings, there has been surprisingly little follow-up work. The primary reason fr the lack of sustained research is that the most reliable manual systems for measuring facial expression often require considerable training and are labor intensive. Automated measurement using computer vision and machine learning seeks to address the need for valid, efficient, and reproducible measurement. Recent systems have shown promise in fairly small studies using posed behavior or structured contexts with confederates, or trained interviewers, or pre-trained (person-specific) face models. For automated coding to be applied in real-world settings, a large data base with ample variability in pose, head motion, skin color, gender, partial occlusion, and expression intensity is needed. We have developed a unique database that meets this need and the algorithms necessary to enable robust automated coding. The database consists of 720 participants in three-person groups engaged in a group formation task. In a preliminary study, we demonstrated that our algorithms can successfully code two key facial signals associated with human emotion in this relatively unconstrained context (Cohn &Sayette, 2010). To achieve efficient, accurate, and valid measurement of facial expression usable in research and clinical settings, we aim to 1) train and validate classifiers to achieve reliable facial expression detectin across this unprecedentedly large, diverse data set;2) extend the previous person-specific methods to person-independent (generic) facial feature detection, tracking, and alignment;and 3) make these tools available for research and clinical use.

Public Health Relevance

The project has two target application domains. For behavioral science, automated facial expression analysis will provide researchers with powerful tools to examine basic questions in emotion and interpersonal processes, as well as emotion processes underlying diverse forms of psychopathology and neurologic disorder. For clinical use, automated facial expression analysis will help clinicians to assess vulnerability and protective factors and objectively evaluate course of treatment across a wide range of disorders including major depression, bipolar disorder, schizophrenia, anxiety, addiction, suicide risk, and pain.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Social Psychology, Personality and Interpersonal Processes Study Section (SPIP)
Program Officer
Simmons, Janine M
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pittsburgh
Schools of Arts and Sciences
United States
Zip Code
Dibeklioglu, Hamdi; Hammal, Zakia; Cohn, Jeffrey F (2017) Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding. IEEE J Biomed Health Inform :
Girard, Jeffrey M; C Wright, Aidan G (2017) DARMA: Software for dual axis rating and media annotation. Behav Res Methods :
Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F et al. (2017) A Branch-and-Bound Framework for Unsupervised Common Event Discovery. Int J Comput Vis 123:372-391
Zeng, Jiabei; Chu, Wen-Sheng; De la Torre, Fernando et al. (2016) Confidence Preserving Machine for Facial Action Unit Detection. IEEE Trans Image Process :
Chu, Wen-Sheng; de la Torre, Fernando; Cohn, Jeffrey (2016) Selective Transfer Machine for Personalized Facial Expression Analysis. IEEE Trans Pattern Anal Mach Intell :
Girard, Jeffrey M; Cohn, Jeffrey F (2015) Automated Audiovisual Depression Analysis. Curr Opin Psychol 4:75-79
Dibeklio?lu, Hamdi; Hammal, Zakia; Yang, Ying et al. (2015) Multimodal Detection of Depression in Clinical Interviews. Proc ACM Int Conf Multimodal Interact 2015:307-310
Girard, Jeffrey M; Cohn, Jeffrey F; Jeni, Laszlo A et al. (2015) Spontaneous facial expression in unscripted social interactions can be measured automatically. Behav Res Methods 47:1136-1147
Girard, Jeffrey M; Cohn, Jeffrey F; Jeni, László A et al. (2015) How much training data for facial action unit detection? IEEE Int Conf Autom Face Gesture Recognit Workshops 1:
De la Torre, Fernando; Chu, Wen-Sheng; Xiong, Xuehan et al. (2015) IntraFace. IEEE Int Conf Autom Face Gesture Recognit Workshops 1:

Showing the most recent 10 out of 24 publications