PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automatic facial feature extraction for face recognition
Paola Campadelli, Raffaella Lanzarotti and Giuseppe Lipori
In: Face Recognition (2007) I-Tech Education and Publishing , Vienna, Austria , pp. 31-58. ISBN ISBN 978-3-902613-03-5

Abstract

Facial feature extraction consists in localizing the most characteristic face components (eyes, nose, mouth, etc.) within images that depict human faces. This step is essential for the initialization of many face processing techniques like facetracking, facial expression recognition or face recognition. Among these, face recognition is a lively research area where it has been made a great effort in the last years to design and compare different techniques. In this chapter we intend to present an automatic method for facial feature extraction that we use for the initialization of our face recognition technique. In our notion, to extract the facial components equals to locate certain characteristic points, e.g. the center and the corners of the eyes, the nose tip, etc. Particular emphasis will be given to the localization of the most representative facial features, namely the eyes, and the locations of the other features will be derived from them. An important aspect of any localization algorithm is its precision. The face recognition techniques (FRTs) presented in literature only occasionally face the issue and rarely state the assumptions they make on their initialization; many simply skip the feature extraction step, and assume perfect localization by relying upon manual annotations of the facial feature positions. However, it has been demonstrated that face recognition heavily suffers from an imprecise localization of the face components. This is the reason why it is fundamental to achieve an automatic, robust and precise extraction of the desired features prior to any further processing. In this respect, we investigate the behavior of two FRTs when initialized on the real output of the extraction method.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:3536
Deposited By:Giuseppe Lipori
Deposited On:11 February 2008