A new approach to face verification from 3D data is presented. The method uses 3D registration techniques designed to work with resolution levels typical of the irregular point cloud representations provided by Structured Light scanning. Preprocessing using a-priori information of the human face and the Iterative Closest Point algorithm are employed to establish correspondence between test and ...

We extend the mass-constraint data clustering and vector quantization algorithm to estimate Gaussian Mixture Models (GMMs) as image features applying to the image retrieval problems. The GMM feature is an alternative method to histograms to represent data density distributions. Histograms are well known for their advantages including rotation invariance, low calculation load, and so on. The GMM...

The local robustness properties of generalized method of moments (GMM) estimators and of a broad class of GMM based tests are investigated in a uni"ed framework. GMM statistics are shown to have bounded in#uence if and only if the function de"ning the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to hav...

This paper compares the performance of three recently proposed estimators for dynamic panel data models (LSDV bias-corrected, MLE and MDE) along with GMM. Using Monte-Carlo, we find that MLE and biascorrected estimators have the smallest bias and are good alternatives for the GMM. System-GMM outperforms the rest in ‘difficult’ designs. Unfortunately, bias-corrected estimator is not reliable in ...

We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as suppo...