Authors: Hailiang Li,Kin-Man Lam,Man-Yau Chiu,Kangheng Wu,Zhibin Lei
ArXiv: 1611.09956
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Abstract URL: http://arxiv.org/abs/1611.09956v1
The constrained local model (CLM) proposes a paradigm that the locations of a
set of local landmark detectors are constrained to lie in a subspace, spanned
by a shape point distribution model (PDM). Fitting the model to an object
involves two steps. A response map, which represents the likelihood of the
location of a landmark, is first computed for each landmark using local-texture
detectors. Then, an optimal PDM is determined by jointly maximizing all the
response maps simultaneously, with a global shape constraint. This global
optimization can be considered as a Bayesian inference problem, where the
posterior distribution of the shape parameters, as well as the pose parameters,
can be inferred using maximum a posteriori (MAP). In this paper, we present a
cascaded face-alignment approach, which employs random-forest regressors to
estimate the positions of each landmark, as a likelihood term, efficiently in
the CLM model. Interpretation from CLM framework, this algorithm is named as an
efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in
each stage of the regressors, the PDM non-rigid parameters of previous stage
can work as shape clues for training each stage regressors. Experimental
results on benchmarks show our approach achieve about 3 to 5 times speed-up
compared with CLM models and improve around 10% on fitting quality compare with
the same setting regression models.