Brandon M. Smith

Learn More
Despite much research interest in facial landmark estimation in recent years, relatively little work has been done to handle the full range of head poses encountered in the real world (e.g., beyond ±45 • rotation). As a result, the large majority of face alignment algorithms are limited to near fronto-parallel faces, and break down on profile faces. We(More)
44 Driver distraction represents a major safety problem in the United States. Naturalistic driving 45 data, such as SHRP2 Naturalistic Driving Study (NDS) data, provide a new window into driver 46 behavior that promises a deeper understanding than was previously possible. Unfortunately, the 47 current practice of manual coding is infeasible for large(More)
Statistical models such as linear regression drive numerous applications in computer vision and machine learning. The landscape of practical deployments of these formulations is dominated by forward regression models that estimate the parameters of a function mapping a set of p covariates, x , to a response variable, y. The less known alternative, Inverse(More)
BACKGROUND Biofilms occur on a wide variety of surfaces including metals, ceramics, glass etc. and often leads to accumulation of large number of various microorganisms on the surfaces. This biofilm growth is highly undesirable in most cases as biofilms can cause degradation of the instruments and its performance along with contamination of the samples(More)
Despite much interest in face alignment in recent years, the large majority of work has focused on near-frontal faces. Algorithms typically break down on profile faces, or are too slow for real-time applications. In this work we propose an efficient approach to face alignment that can handle 180 degrees of head rotation in a unified way (e.g., without(More)
  • 1