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We investigate the biologically inspired features (BIF) for human age estimation from faces. As in previous bio-inspired models, a pyramid of Gabor filters are used at all positions of the input image for the S1 units. But unlike previous models, we find that the pre-learned prototypes for the S2 layer and then progressing to C2 cannot work well for age estimation. We also propose to use Gabor filters...
We propose a fusion framework to integrate multiple cues for tracking by finding a set of optimal dynamic weights for different tracking modalities. In the setup of Bayesian sequential estimation, we give an optimal criterion to find the dynamic weight for each modality: Using a linear combination of the proposal distributions from multiple cues to approach the posterior distribution p(xt|yt). The...
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. Then, classification is performed...
Head pose estimation has many useful applications in practice. How to estimate the head pose automatically and robustly is still a challenging problem. In pose estimation, different pose angles can be used as regression values or viewed as different class labels. Thus a question is raised in our study: which is proper for pose estimation - classification or regression? We investigate representative...
Human age prediction is useful for many applications. The age information could be used as a kind of semantic knowledge for multimedia content analysis and understanding. In this paper we propose a probabilistic fusion approach (PFA) that produces a high performance estimator for human age prediction. The PFA framework fuses a regressor and a classifier. We derive the predictor based on Bayespsila...
This paper proposes a fully automatic framework for static human head pose estimation. With a 2D human multi-view face image as input, the face region is detected and cropped out. Then the pose of the face is assessed by the pose categories. Based on the appearance of the face region, variant subspace learning methods including principal component analysis (PCA), linear discriminant analysis (LDA),...
Background estimation, i.e. automatic recovery of the background image from a sequence of images containing moving foreground objects, is an important module in many applications, e.g. surveillance and video segmentation. In this paper, we present a simple, yet effective and robust approach for background estimation based on loopy belief propagation. Robustness of the proposed approach means: (i)...
In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian mixture models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the...
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