Automatic tracking of facial features in patients with facial neuromuscular dysfunction

Facial neuromuscular dysfunction severely impacts adaptive and expressive behavior and emotional health. Appropriate treatment is aided by quantitative and efficient assessment of facial motion impairment. We validated a newly developed method of quantifying facial motion, automated face analysis (AFA), by comparing it with an established manual marking method, the Maximal Static Response Assay (MSRA). In the AFA, motion of facial features is tracked automatically by computer vision without the need for placement of physical markers or restrictions of rigid head motion. Nine patients (seven women and two men) with a mean age of 39.3 years and various facial nerve disorders (five with Bell’s palsy, three with trauma, and one with tumor resection) participated. The patients were videotaped while performing voluntary facial action tasks (brow raise, eye closure, and smile). For comparison with MSRA, physical markers were placed on facial landmarks. Image sequences were digitized into 640 3 480 3 24-bit pixel arrays at 30 frames per second (1 pixel >0.3 mm). As defined for the MSRA, the coordinates of the center of each marker were manually recorded in the initial and final digitized frames, which correspond to repose and maximal response. For the AFA, these points were tracked automatically in the image sequence. Pearson correlation coefficients were used to evaluate consistency of measurement between manual (the MSRA) and automated (the AFA) tracking methods, and paired t tests were used to assess the mean difference between methods for feature tracking. Feature measures were highly consistent between methods, Pearson’s r 5 0.96 or higher, p , 0.001 for each of the action tasks. The mean differences between the methods were small; the mean error between methods was comparable to the error within the manual method (less than 1 pixel). The AFA demonstrated strong concurrent validity with the MSRA for pixel-wise displacement. Tracking was fully automated and provided motion vectors, which may be useful in guiding surgical and rehabilitative approaches to restoring facial function in patients with facial neuromuscular disorders. (Plast. Reconstr. Surg. 107: 1124, 2001.) Facial neuromuscular dysfunction severely impacts adaptive and expressive behavior and emotional well-being. The patient with a facial neuromuscular disorder often has difficulty performing basic daily functions such as eating, drinking, and swallowing, and communicating his or her feelings and intentions to other persons. The risk for moderate to serious levels of depression and anxiety is heightened in this person.1 The treatment of facial nerve disorders requires an accurate assessment of impairment2–4 and dysfunction5 to determine indications and to plan for interventions for recovery of normal facial motion.6 Rating scales of facial neuromotor impairment have been the primary clinical tool for assessing and describing outcomes of facial nerve disorders.7–10 Although useful, clinical ratings are inherently subjective, subject to drift among raters, and susceptible to bias, and they lack precise description of facial impairment. They provide an efficient but necessarily crude measure of severity and progression of impairment and recovery.