Feature detection and tracking with constrained local models

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the AAM [1]. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our Constrained Local Model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.

Bibliographical metadata

Original languageEnglish
Title of host publicationBMVC 2006 - Proceedings of the British Machine Vision Conference 2006|BMVC - Proc. Br. Mach. Vis. Conf.
Place of PublicationEdinburgh
PublisherBMVA Press
Number of pages9
Publication statusPublished - 2006
Event2006 17th British Machine Vision Conference, BMVC 2006 - Edinburgh
Event duration: 1 Jul 2006 → …


Conference2006 17th British Machine Vision Conference, BMVC 2006
Period1/07/06 → …