Code & Data


Invariance and Reliability in Statistical Shape Models


F.M. Sukno

Instituto de Investigación en Ingeniería de Aragón

Universidad de Zaragoza, 2008


The thesis concentrates on the applicability of statistical modeling to facial analysis. Starting from the paradigm of shape and appearance models developed in the last decade, new algorithms are proposed to allow improving their reliability and invariance to different types of rotations. The extensions are formulated in a generic way, such that the models keep the wide applicability of the original approach.

The proposed techniques were experimentally validated in facial analysis tasks. This field became especially relevant in the last few years with an important growth of its international market. A remarkable fact in this sense is the recent adoption of facial biometrics as the standard technology for new biometric passports, taking over other important biometric modalities such as iris or fingerprints. Although the latter are able to achieve lower error rates, the face appearance is the natural way for identification among humans and it is perceived less intrusive. Additionally, it is among the very few biometric modalities that can work, in theory, without the explicit collaboration of the person to be identified.

Chapter 1 provides a brief overview on biometrics and presents the components of a generic biometric system for facial recognition, with especial focus on shape and appearance models. Specifically, Active Shape Models (ASMs) constitute the key methodological component of this thesis, and are briefly covered in Chapter 2.

ASMs allow for the automatic segmentation and analysis of images based on generative models. Introduced in 1992 (by T. Cootes et al.), a considerable number of works has been published on the application of ASMs to diverse types of images, among which medical and facial images are the most numerous. On the other hand, ASMs proofed themselves too simple for modeling purposes in some applications. As a result, later publications focused on extensions and improvements to the original formulation. One of the most important was the introduction of the Active Appearance Models (AAMs) in 1998. The AAMs soon became popular enough to be considered a separated methodology in its own right, independently from ASMs.

This thesis introduces three novel extensions to ASM. They aim at improving the behavior of these models with a special focus on invariance and reliability. The hypothesis is that the extension and improvement of the segmentation algorithms will lead to a more accurate delineation of facial features, allowing for a more appropriate extraction of image information.

Our first extension addresses the problem of accurate segmentation of prominent features of the face in frontal shots, and is covered in Chapter 3. We propose a method that generalizes linear ASMs using a non-linear intensity model and incorporating a reduced set of differential invariant features as local image descriptors. These features are invariant to rigid transformations, and a subset of them is chosen by Sequential Feature Selection. The new approach overcomes the unimodality and Gaussianity assumptions of classical ASMs regarding the distribution of the intensity values across the training set. Our methodology has demonstrated a significant improvement in segmentation accuracy when compared to the linear ASM, which also derived in lower error rates on identity verification tasks.

The second extension (Chapter 4) concentrates on the invariance of the matching algorithm in the presence of out-of-plane rotations when working with quasi-planar objects. By constraining the analysis to certain parts of the face, the outlines can be approximately considered coplanar. Then, based on projective geometry concepts, ASMs are modified so that they can work independently from the viewpoint (within the range limitations of feature visibility). As a consequence, an ASM constructed with frontal view images can be directly applied to the segmentation of pictures taken from other viewpoints. Validation of the method is presented in images systematically divided into three different rotations (to both sides), as well as upper and lower views due to nodding. The presented tests are among the largest quantitative results reported to date in face segmentation under varying poses.

The third extension (Chapter 5) provides an automatic reliability measure of the segmentation for each analyzed image. That is, the model is able to estimate whether the segmentation obtained for certain image is trustworthy or not. This is very important when ASMs are used into fully-automatic systems, since accurate segmentation is crucial for the subsequent interpretation of the image. The automatic estimation of reliability can be promising for a number of applications. We demonstrate two of them: automatic model selection and reliable identity verification. Results were highly satisfactory in both cases. The strength of the proposed approach relies on its low false positive rate, which means that incorrect segmentations are very unlikely to be misclassified as reliable.

In this way, the first two extensions share the concept of invariance (to rotations in and out of the image plane). On the other hand, it will be shown that the first extension also increases the accuracy of the segmentation, while the third extension is devoted to the estimation of how reliable is the segmentation of each image. In all cases, intensive experiments have been performed to validate the proposed algorithms, with encouraging results.


 Full thesis document

 Full thesis document (through i3A link)




PowerPoint presentation


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Example videos





Standard ASM