EthoVision: A versatile video tracking system for automation of behavioral experiments

Brain-derived neurotrophic factor (BDNF) and its receptor TrkB regulate both short-term synaptic functions and long-term potentiation (LTP) of brain synapses, raising the possibility that BDNF/TrkB may be involved in cognitive functions. We have generated conditionally gene targeted mice in which the knockout of the trkB gene is restricted to the forebrain and occurs only during postnatal development. Adult mutant mice show increasingly impaired learning behavior or inappropriate coping responses when facing complex and/or stressful learning paradigms but succeed in simple passive avoidance learning. Homozygous mutants show impaired LTP at CA1 hippocampal synapses. Interestingly, heterozygotes show a partial but substantial reduction of LTP but appear behaviorally normal. Thus, CA1 LTP may need to be reduced below a certain threshold before behavioral defects become apparent.

Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AUs) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AUs and 10 lower face AUs) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AUs and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AUs. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams.