6,442 research outputs found
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.Comment: 12 pages, 5 figures, 3 table
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables
In this work we address the problem of approximating high-dimensional data
with a low-dimensional representation. We make the following contributions. We
propose an inverse regression method which exchanges the roles of input and
response, such that the low-dimensional variable becomes the regressor, and
which is tractable. We introduce a mixture of locally-linear probabilistic
mapping model that starts with estimating the parameters of inverse regression,
and follows with inferring closed-form solutions for the forward parameters of
the high-dimensional regression problem of interest. Moreover, we introduce a
partially-latent paradigm, such that the vector-valued response variable is
composed of both observed and latent entries, thus being able to deal with data
contaminated by experimental artifacts that cannot be explained with noise
models. The proposed probabilistic formulation could be viewed as a
latent-variable augmentation of regression. We devise expectation-maximization
(EM) procedures based on a data augmentation strategy which facilitates the
maximum-likelihood search over the model parameters. We propose two
augmentation schemes and we describe in detail the associated EM inference
procedures that may well be viewed as generalizations of a number of EM
regression, dimension reduction, and factor analysis algorithms. The proposed
framework is validated with both synthetic and real data. We provide
experimental evidence that our method outperforms several existing regression
techniques
The impact of price policy on demand for alcohol in rural India
Whether raising the price of addictive goods can reduce its burden is widely debated in many countries, largely due to lack of appropriate data and robust methods. Three key concerns frequently raised in the literature are: unobserved heterogeneity; omitted variables; identification problem. Addressing these concerns, using robust instrument and employing unique individual-level panel data from Indian Punjab, this paper investigates two related propositions (i) will increase in alcohol price reduce its burden (ii) since greater incomes raise the costs of inebriation, will higher incomes affect consumption of alcohol negatively. Distinct from previous studies, the key variable of interest is the budget share of alcohol that allows studying the burden of alcohol consumption on drinker's and also on other family members. Results presented show that an increase in alcohol price is likely to be regressive, especially on the bottom quartile, with a rise in the budget share of alcohol given budget constraint. This outcome is robust to different econometric specifications. Preliminary explorations suggest that higher per capita income increases the odds of quitting drinking. Results reported have wider implications for the effective design of addiction related health policies
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore their eyes are not always visible. Consequently both gaze
and VFOA estimation cannot be based on eye detection and tracking. We propose a
method that exploits the correlation between eye gaze and head movements. Both
VFOA and gaze are modeled as latent variables in a Bayesian switching
state-space model. The proposed formulation leads to a tractable learning
procedure and to an efficient algorithm that simultaneously tracks gaze and
visual focus. The method is tested and benchmarked using two publicly available
datasets that contain typical multi-party human-robot and human-human
interactions.Comment: 15 pages, 8 figures, 6 table
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