Feature Extraction and Representation for Face Recognition
Mohammad Nazmus Saquib, Olaf Hellwich, Zahid Riaz · InTech eBooks · 2010
A comprehensive account of almost all the feature extraction methods used in current face recognition systems is presented. Specifically we have made distinction in the holistic and local feature extraction and differentiate them qualitatively as opposed to quantitatively. It is argued that a global feature representation should be preferred over a bag-of-feature approach. The problems in current feature extraction techniques and their reliance on a strict alignment is discussed. Finally we have introduced to use face-GLOH signatures that are invariant with respect to scale, translation and rotation and therefore do not require properly aligned images. The resulting dimensionality of the vector is also low as compared to other commonly used local features such as Gabor, LBP etc. and therefore learning based methods can also benefit from it. In a typical multi-view face recognition task, where it is assumed to have several examples of a subject available for training, we have shown in an extensive experimental setting the advantages and weaknesses of commonly used feature descriptions. Our results show that under more realistic assumptions, most of the classifiers failed on conventional features. While using the introduced face-GLOH-signature representation is relatively less affected by large in-class variations. This has been demonstrated by providing a fair performance comparison of several classifiers under more practical conditions such as misalignments, large number of subjects and large pose variations. An important conclusion is to be drawn from the results on FERET is that conventional multi-view face recognition cannot cope well with regards to large pose variations. Even using a large number of training examples in different poses for a subject do not suffice for a satisfactory recognition. In order to solve the problem where only one training example per subject is available, many recent methods propose to use image synthesis to generate a given subject at all other views and then perform a conventional multi-view recognition (Beymer and Poggio, 1995; Gross et al, 2004). Besides the fact that such synthesis techniques cause severe artefacts and thus cannot preserve the identity of an individual, a conventional classification cannot yield good recognition results, as has been shown in an extensive experimental setting. More sophisticated methods are therefore needed in order to address pose invariant face recognition. Large pose differences cause significant appearance variations that in general are larger than the appearance variation due to identity. One possible way of addressing this is to learn these variations across each pose, more specifically by fixing the pose and establishing a correspondence on how a person’s appearance changes under this pose one could reduce the in-class appearance variation significantly. In our very recent work (Sarfraz and Hellwich, 2009), we demonstrate the usefulness of face-GLOH signature in this direction.