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This paper introduces a head pose estimation system that automatically localizes the nose-tips of the faces and estimates head poses in images simultaneously. In the training stage, the nose-tips of the faces are first manually labeled. The appearance variations caused by head pose changes are then characterized by a Tensorposes model. Given an image with unknown head pose and nose-tip location, the nose-tip of the face is automatically localized in a coarse-to-fine fashion after the skin color segmentation. The head pose is also estimated simultaneously. The performance of our system is evaluated on the Pointing'04 head pose image data set. We first evaluate the classification performance of the Tensorposes models with image patches of the faces cropped according to the manually labeled nose-tip locations of the faces in the Pointing '04 data set. By leaving-one-person-out evaluation strategy, we obtain the optimal parameters of the Tensorposes model, and evaluate the discriminative power of the Tensorposes model built based on high order singular value decomposition (HOSVD) and multilinear independent component analysis (MICA), and naive principal component analysis (PCA) subspace models. It is shown Tensorposes model by HOSVD and MICA decomposition performs similarly good but much better than naive PCA subspace models. The Tensorposes model is then utilized to automatically localize nose-tip location in the testing image and to simultaneously estimate the head pose. The nose-tip localization and pose estimation accuracy of the proposed system are evaluated against the ground truth. Finally, cross-database evaluation of the performance of our system is carried out on Pointing'04 database, a selected subset of CMU PIE database, and some pictures from CLEAR'07 head pose evaluation database. The experiments show that our system generalizes reasonably well to the real-world scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid dimension reduction technique for classification is proposed based on the hybrid analysis of principal component analysis (PCA) and linear discriminant analysis (LDA). LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known for preserving the most descriptive ones after projection. Our hybrid technique integrates discriminant and descriptive information and finds a richer set of alternatives beyond LDA and PCA in a 2-D parametric space, which fits a specific classification task and data distribution better. Theoretical study shows that our technique also alleviates the singularity problem of scatter matrix, which is caused by small training set, and increases the effective dimension of the projected subspace. In order to find the hybrid features adaptively and avoid exhaustive parameter searching, we further propose a boosted hybrid analysis method that incorporates a nonlinear boosting process to enhance a set of hybrid classifiers and combine them into a more accurate one. Compared with the other techniques that aim at combining PCA and LDA, our approaches are novel because our method finds alternatives to LDA and PCA in a 2-D parameter space and the boosting process provides enhancement and robust combination of the classifiers. Extensive experiments are conducted on benchmark and real image databases to compare our proposed methods with the state-of-the-art linear and nonlinear discriminant analysis techniques. The results show the superior performance of our hybrid analysis methods. [ABSTRACT FROM AUTHOR]

Presents relevant feedback from two characteristics of content-based image retrieval (CBIR). Details on the experimental results; Description of the multimedia object model; Integrating relevance feedback in CBIR.