3,881 research outputs found
Multi-stream gaussian mixture model based facial feature localization=Çoklu gauss karışım modeli tabanlı yüz öznitelikleri bulma algoritması
This paper presents a new facial feature localization system which estimates positions of eyes, nose and mouth corners simultaneously. In contrast to conventional systems, we use the multi-stream Gaussian mixture model (GMM) framework in order to represent structural and appearance information of facial features. We construct a GMM for the region of each facial feature, where the principal component analysis is used to extract each facial feature. We also build a GMM which represents the structural information of a face, relative positions of facial features. Those models are combined based on the multi-stream framework. It can reduce the computation time to search region of interest (ROI). We demonstrate the effectiveness of our algorithm through experiments on the BioID Face Database
Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification
Accurate, robust, inexpensive gaze tracking in the car can help keep a driver
safe by facilitating the more effective study of how to improve (1) vehicle
interfaces and (2) the design of future Advanced Driver Assistance Systems. In
this paper, we estimate head pose and eye pose from monocular video using
methods developed extensively in prior work and ask two new interesting
questions. First, how much better can we classify driver gaze using head and
eye pose versus just using head pose? Second, are there individual-specific
gaze strategies that strongly correlate with how much gaze classification
improves with the addition of eye pose information? We answer these questions
by evaluating data drawn from an on-road study of 40 drivers. The main insight
of the paper is conveyed through the analogy of an "owl" and "lizard" which
describes the degree to which the eyes and the head move when shifting gaze.
When the head moves a lot ("owl"), not much classification improvement is
attained by estimating eye pose on top of head pose. On the other hand, when
the head stays still and only the eyes move ("lizard"), classification accuracy
increases significantly from adding in eye pose. We characterize how that
accuracy varies between people, gaze strategies, and gaze regions.Comment: Accepted for Publication in IET Computer Vision. arXiv admin note:
text overlap with arXiv:1507.0476
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