The goal of this paper is to find sparse and representative spatial priors that can be applied to part-based object localization. Assuming a GMRF prior over part configurations, we construct the graph structure of the prior by regressing the position of each part on all other parts, and selecting the neighboring edges using a Lasso-based method. This approach produces a prior structure which is not only sparse, but also faithful to the spatial dependencies that are observed in training data. We evaluate the representation power of the learned prior structure in two ways: first is drawing samples from the prior, and comparing them with the samples produced by the GMRF priors of other structures; second is comparing the results when applying different priors to a facial components localization task. We show that the learned graph captures meaningful geometrical variations with significantly sparser structure and leads to better parts localization results.

We present an approach for aligning a 3D deformable model to a single face image. The model consists of a set of sparse 3D points and the view-based patches associated with every point. Assuming a weak perspective projection model, our algorithm iteratively deforms the model and ad- justs the 3D pose to fit the image. As opposed to previous approaches, our algorithm starts the fitting without resort- ing to manual labeling of key facial points. And it makes no assumptions about global illumination or surface prop- erties, so it can be applied to a wide range of imaging con- ditions. Experiments demonstrate that our approach can effectively handle unseen faces with a variety of pose and illumination variations.

In this paper we study the problem of shape analysis and its application in locating facial feature points on frontal faces. We propose a Bayesian inference solution based on tangent shape approximation called Bayesian tangent shape model (BTSM). Similarity transform coefficients and the shape parameters in BTSM are determined through MAP estimation. Tangent shape vector is treated as the hidden state of the model, and accordingly, an EM based searching algorithm is proposed to implement the MAP procedure. The major results of our algorithm are: 1) tangent shape is updated by a weighted average of two shape vectors, the projection of the observed shape onto tangent space, and the reconstruction of shape parameters. 2) Shape parameters are regularized by multiplying a ratio of the noise variations, which is a continuous function instead of a truncated function. We discussed the advantages conveyed by these results, and demonstrate the accuracy and the stability of the algorithm by extensive experiments.

This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are learnt from the training set instead of arbitrarily defined. The learning procedure consists of two steps. First, a modified version of NMF (non-negative matrix factorization), namely local NMF (LNMF), is applied to obtain an overcomplete set of local features. Second, a learning algorithm based on AdaBoost is used to select a small number of local features and yields extremely efficient classifiers. Experiments are presented which show that face detection performance is comparable to state-of-the-art face detection systems.

Modeling subspaces of a distribution of interest in high dimensional spaces is a challenging problem in pattern analysis. In this paper, we present a novel framework for pose invariant face detection through. multi-view face distribution modeling. The approach is aimed to learn a set of low-dimensional subspaces from an originally nonlinear distribution by using the mixtures of probabilistic PCA. From the experiments, we found the learned PPCA models are of low dimensionality and exhibit high local linearity, and consequently offer an efficient representation for visual recognition. The model is then used to extract features and select "representative" negative training samples. Multi-view face detection is performed in the derived feature space by classifying each face into one of the view classes or into the nonface class, by using a multi-class SVM array classifier. The classification results from each view are fused together and yields the final classification results. The experimental results demonstrate the performance superiority of our proposed framework while performing multi-view face detection.

Face images are subject to changes in view and illumination. Such changes cause data distribution to be highly nonlinear and complex in the image space. It is desirable to learn a nonlinear mapping from the image space to a low dimensional space such that the distribution becomes simpler tighter and therefore more predictable for better modeling effaces. In this paper we present a kernel machine based approach for learning such nonlinear mappings. The aim is to provide an effective view-based representation for multi-view face detection and pose estimation. Assuming that the view is partitioned into a number of distinct ranges, one nonlinear view-subspace is learned for each (range of) view from a set of example face images of that view (range), by using kernel principal component analysis (KPCA). Projections of the data onto the view-subspaces are then computed as view-based nonlinear features. Multi-view face detection and pose estimation are performed by classifying a face into one of the facial views or into the nonface class, by using a multi-class kernel support vector classifier (KSVC). Experimental results show that fusion of evidences from multi-views can produce better results than using the result from a single view; and that our approach yields high detection and low false alarm rates in face detection and good accuracy in pose estimation, in comparison with the linear counterpart composed of linear principal component analysis (PCA) feature extraction and Fisher linear discriminant based classification (FLDC).

We present a system developed for content-based broadcast news video browsing for home users. There are three main factors that distinguish our work from other similar ones. First, we have integrated the image and audio analysis results in identifying news segments. Second, we use the video OCR technology to detect text from frames, which provides a good source of textual information for story classification when transcripts and close captions are not available. Finally, natural language processing (NLP) technologies are used to perform automated categorization of news stories based on the texts obtained from close caption or video OCR process. Based on these video structure and content analysis technologies, we have developed two advanced video browsers for home users: intelligent highlight player and HTML-based video browser.