DASM: An open source active shape model for automatic registration of objects

Abstract

The main contribution of this paper is to introduce DASM - Dynamic Active Shape Models, an open source software for the automatic detection of fiducial points on objects for subsequent registration, to the research community. DASM leverages the tremendous work of STASM, a well known software library for automatic detection of points on faces. In this work we compare DASM to other well-known techniques for automatic face registration: Active Appearance Models (AAM) and Constrained Local Models (CLM). Further we show that DASM outperforms these techniques on a per registration-point error, average object error, and on cumulative error distribution. As a follow on, we show that DASM outperforms STASM v3.1 on model training and registration by leveraging open source libraries for computer vision (OpenCV v2.4) and threading/parallelism (OpenMP). The improvements in speed and performance of DASM allows for extremely dense registration, 252 points on the face, in video applications.