Facial feature extraction using a probabilistic approach

Facial features such as lip corners, eye corners and nose tip are critical points in a human face. Robust extraction of such facial feature locations is an important problem which is used in a wide range of applications. In this work, we propose a probabilistic framework and several methods which can extract critical points on a face using both location and texture information. The new framework enables one to learn the facial feature locations probabilistically from training data. The principle is to maximize the joint distribution of location and apperance/texture parameters. We first introduce an independence assumption which enables independent search for each feature. Then, we improve upon this model by assuming dependence of location parameters but independence of texture parameters. We model location parameters with a multi-variate Gaussian and the texture parameters are modeled with a Gaussian mixture model which are much richer as compared to the standard subspace models like principal component analysis. The location parameters are found by solving a maximum likelihood optimization problem. We show that the optimization problem can be solved using various search strategies. We introduce local gradient-based methods such as gradient ascent and Newton's method initialized from independent model locations both of which require certain non-trivial assumptions to work. We also propose a multi-candidate coordinate ascent search and a coarse-to-fine search strategy which both depend on efficiently searching among multiple candidate points. Our framework is compared in detail with the conventional statistical approaches of active shape and active appearance models. We perform extensive experiments to show that the new methods outperform the conventional approaches in facial feature extraction accuracy.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Facial feature extraction using a novel probabilistic method. ► Joint modeling of location and local texture. ► Gaussian mixture model for modeling local texture features. ► Efficient and innovative gradient based and multiple candidate based search methods. ► Comparison with the state of the art methods of ASM and AAM.