Automatic facial analysis for objective assessment of facial paralysis

Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Optimization of a microarray-based biosensor for detection of viral pathogens

Rapid and sensitive detection of viral infections is of significant importance for improving patient care and containing outbreaks that threaten public health. Although there has been an enormous effort to develop point-of-care biosensors for viral diagnostics applications, sensitive, robust and easily portable platforms have yet to be realized. This dissertation focuses on optimization of a multiplexed immunoassay platform for viral diagnostics applications using a label-free optical biosensor termed Single-Particle Interferometric Reflectance Imaging Sensor (SP-IRIS). SP-IRIS utilizes an antibody microarray that captures the target viruses and an optical instrument that allows visualization of individual captured virus particles. Since this platform relies on capture of whole viruses, it is crucial to identify high-affinity antibodies that are capable of recognizing intact virions. For this purpose, we screened various antibodies for their performance on the SP-IRIS platform. By screening 43 different antibodies for three different viruses, we demonstrated specific and sensitive detection of different viruses and different subtypes of the same virus. This work allowed us to assemble an antibody microarray capable of multiplexed detection that has been tested in our laboratory as well as at two separate high-containment facilities. Next, we adapted a different antibody immobilization technique, DNA-directed antibody immobilization (DDI), to the SP-IRIS platform as a means to improve the sensitivity and robustness of the assay. First, we characterized the elevation of the antibodies conjugated to a DNA sequence on a three-dimensional polymeric surface using a fluorescence axial localization technique, Spectral Self-Interference Fluorescence Microscopy, determining the optimal length of the DNA linkers for SP-IRIS substrates. We subsequently showed the specific detection of Vesicular Stomatitis Virus (VSV) expressing Ebola glycoprotein on SP-IRIS platform using the DDI approach. We showed that DNA-conjugated antibodies improve the capture efficiency resulting in over a ten-fold improvement in assay sensitivity compared to directly immobilized antibodies. To demonstrate the feasibility of the DDI technique for multiplexed virus detection utilizing SP-IRIS, we used VSVs expressing Ebola, Marburg or Lassa surface glycoproteins and successfully demonstrated specific and multiplexed detection using a DNA microarray surface. We also combined this approach with a passive microfluidic cartridge, demonstrating the feasibility of SP-IRIS as a rapid testing technique that is well suited for point-of-care applications