Face Pose Estimation in Uncontrolled Environments

Automatic estimation of head pose faciliates human facial analysis. It has widespread applications such as, gaze direction detection, video teleconferencing and human computer interaction (HCI). It can also be integrated in a multi-view face detection and recognition system. Most current methods estimate pose in a limited range or treat pose as a classification problem by assigning the face to one of many discrete poses [1,2]. Mainly tested on images taken in controlled environments e.g. the FacePix dataset [3] (Fig. 1a)

Mosaicfaces: a discrete representation for face recognition

Most face recognition algorithms use a “distancebased” approach: gallery and probe images are projected into a low dimensional feature space and decisions about matching are based on distance in this space. In this paper we use a very different representation, where each face is approximated by a regular grid of patches (a mosaicface). Each of these patches is chosen from a library. Faces are now represented as a list of indices to this library. Since there is no obvious way to measure distance between two such lists, we use a probabilistic approach in which the observed face data is explained by a generative model. There are two phases: (i) Learning- we estimate library contents and associated variability (noise). (ii) Recognition- we evaluate the probability that probe and gallery images were generated from the same library patches. Our method performs significantly better than contemporary approaches, in the presence of large illumination changes. Variation in viewing conditions, is handled by extending this model to learn equivalences between multiple patch appearances. We demonstrate that this method provides major performance improvement on the lighting subset of the XM2VTS database, compared to ”distance-based ” methods. 1

Patch-based WithinObject Classification

Advances in object detection have made it possible to collect large databases of certain objects. In this paper we exploit these datasets for within-object classification. For example, we classify gender in face images, pose in pedestrian images and phenotype in cell images. Previous work has mainly targeted the above tasks individually using object specific representations. Here, we propose a general Bayesian framework for within-object classification. Images are represented as a regular grid of non-overlapping patches. In training, these patches are approximated by a predefined library. In inference, the choice of approximating patch determines the classification decision. We propose a Bayesian framework in which we marginalize over the patch frequency parameters to provide a posterior probability for the class. We test our algorithm on several challenging “real world ” databases. 1

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the sub-populations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T-2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS). Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved