A Monte Carlo strategy to integrate detection and model-based face analysis
Schönborn S, Forster A, Vetter T, Egger B (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8142 LNCS
Pages Range: 101-110
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: DEU
DOI: 10.1007/978-3-642-40602-7_11
Abstract
We present a novel probabilistic approach for fitting a statistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined using a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable detections and therefore does not need the detectors to take an early and possible wrong hard decision before fitting. Second, it is open for integration of various cues to guide the fitting process. Based on the proposed approach, we implemented a completely automatic, pose and illumination invariant face recognition application. We are able to train and test the building blocks of our application on different databases. The system is evaluated on the Multi-PIE database and reaches state of the art performance. © 2013 Springer-Verlag.
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Switzerland (CH)How to cite
APA:
Schönborn, S., Forster, A., Vetter, T., & Egger, B. (2013). A Monte Carlo strategy to integrate detection and model-based face analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 101-110). DEU.
MLA:
Schönborn, Sandro, et al. "A Monte Carlo strategy to integrate detection and model-based face analysis." Proceedings of the 35th German Conference on Pattern Recognition, GCPR 2013, DEU 2013. 101-110.
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