An Analysis of the Sensitivity of Active Shape Models to Initialization When Applied to Automatic Facial Landmarking
Active Shape Models (ASMs) have recently gained popularity for performing automatic facial landmark fitting. Their demonstrated ability to generalize and fit unseen faces make them ideal candidates for this task unlike the traditional Active Appearance Model (AAM)-based approaches, which have difficulty in accurately landmarking unseen images. Given a test image, a face detector is used to determine the locations, orientations and sizes of faces in the image. Facial landmarking algorithms, such as ASMs, are initialized based on these parameters. In this paper, we conduct a series of experiments to exhaustively evaluate the tolerance of three popular ASMs to initialization perturbations (translation, rotation, and scaling in size) of the face detected, a topic that has not been analyzed in depth to date. Our results are consistent across different databases, provide an understanding of the role initialization plays in the landmark fitting process and serve as a performance gauge that could be considered when comparing facial landmarking algorithms.