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Prolonged impairment of protective ocular functions can compromise vision and lead to blindness if uncorrected. Several facial transplants have incorporated periorbital structures with variable eyelid preservation, but objective assessment of post-transplant periorbital function has been limited.Kinematic data were collected from a full-face recipient that included the fist total eyelid transplantation at 5 separate pre-transplant (PRE) and post-transplant time points (T1-T4). Using optical facial tracking, eyelid movements were tracked during involuntary blinking and compared with controls.There was significant improvement in right eye aperture from PRE to T1 (β = 5.54, P 0.001), with no change between T1 and T4. Aperture fluctuated in the left eye, with a temporary decrease between T2 and T3 corresponding with revision brow lift (β = -4.57, P 0.001). Although improved from the pre-transplantation, right and left eye apertures remained significantly smaller than controls at T1 and T4 (P 0.001). Similarly, spatial coupling increased from PRE to T1 (β = 0.63, P 0.001) and remained high at T4, albeit significantly less than controls (P 0.001). Temporal coupling improved from PRE to T2 (β = 2.29, P 0.02) and was sustained at subsequent time points, with no difference relative to controls at T4. Considerable improvement was observed on clinical examination, with full functional status.Application of a novel method for assessing functional eyelid recovery using facial tracking technology to the first total eyelid transplantation in the setting of a full facial transplant shows clear functional improvement after transplantation and suggests revisions can be performed safely to optimize aesthetic outcomes without permanent negative functional impact.

In this paper, we present a system for real-time performance-driven facial animation. With the system, the user can control the facial expression of a digital character by acting out the desired facial action in front of an ordinary camera. First, we create a muscle-based 3D face model. The muscle actuation parameters are used to animate the face model. To increase the reality of facial animation, the orbicularis oris in our face model is divided into the inner part and outer part. We also establish the relationship between jaw rotation and facial surface deformation. Second, a real-time facial tracking method is employed to track the facial features of a performer in the video. Finally, the tracked facial feature points are used to estimate muscle actuation parameters to drive the face model. Experimental results show that our system runs in real time and outputs realistic facial animations. Compared with most existing performance-based facial animation systems, ours does not require facial markers, intrusive lighting, or special scanning equipment, thus it is inexpensive and easy to use.

We present automated, real-time models built with machine learning algorithms which use videotapes of subjects' faces in conjunction with physiological measurements to predict rated emotion (trained coders' second-by-second assessments of sadness or amusement). Input consisted of videotapes of 41 subjects watching emotionally evocative films along with measures of their cardiovascular activity, somatic activity, and electrodermal responding. We built algorithms based on extracted points from the subjects' faces as well as their physiological responses. Strengths of the current approach are (1) we are assessing real behavior of subjects watching emotional videos instead of actors making facial poses, (2) the training data allow us to predict both emotion type (amusement versus sadness) as well as the intensity level of each emotion, (3) we provide a direct comparison between person-specific, gender-specific, and general models. Results demonstrated good fits for the models overall, with better performance for emotion categories than for emotion intensity, for amusement ratings than sadness ratings, for a full model using both physiological measures and facial tracking than for either cue alone, and for person-specific models than for gender-specific or general models.

We propose a novel system that incorporates physiological monitoring as part of the human-computer interface. The sensing element is a thermal camera that is employed as a computer peripheral. Through bioheat modeling of facial imagery almost the full range of vital signs can be extracted, including localize blood flow, cardiac pulse, and breath rate. This physiological information can then be used to draw inferences about a variety of health symptoms and psychological states. Our research aims to realize the notion of desktop health monitoring and create truly collaborative interactions in which humans and machines are both observing and responding.