Jiali Zhao

LBP Discriminant Analysis for Face Verification

This paper presents a novel Local Binary Pattern (LBP) based Kernel Fisher Discriminant Analysis (KFDA) approach by integrating the LBP descriptor of face images and the KFDA method for face classifier. LBP extracts desirable facial features which consider both shape and texture information to cope with the variation due to facial expression and illumination changes. The KFDA method is then extended to take all the advantages of LBP descriptor for improved face verification performance. We introduce the kernel function by using Chi square statistic distance and RBF as inner product for KFDA classifier. The effectiveness of the LBP based KFDA method with Chi square statistic distance as inner product is shown in terms of comparison with original LBP and KFDA methods on FRGC database.

Level set methods, distance function and image segmentation

In the study of level set methods, several significant problems were neglected all along, such as the existence, uniqueness and singularities of level set methods. In this article we give the proof that in a neighborhood of the initial zero level set, for the level set equations with the restriction of distance function, there exists a unique solution, which must be the signed distance junction with respect to the evolving surface. We also present the analysis of singular points effect on level set evolution and give an adaptive narrow banding algorithm. The detailed numerical analysis and a simplified definition for singular points are presented. We give an adaptive narrow banding algorithm, which avoids the singular points and is proved to be robust and efficient in segmentation of CT data and synthesized images.

A novel heat kernel based Monte Carlo localization algorithm

A novel heat kernel based Monte Carlo localization (HK-MCL) algorithm is presented to solve the degeneracy problem of conventional Monte Carlo localization: real-time global localization requires the number of initial samples to be small, whereas global localization may fail if the number of initial samples is small. The degeneracy problem is solved by an optimization approach called heat kernel based perturbation (HK-perturbation), which moves the samples towards the high likelihood area. HK-perturbation integrates the average local density and importance weight of samples to determine each samples perturbation probability. The strategy improves simulated annealing algorithm via an obvious form of temperature, both in time and space, with respect to average local density and importance weight of samples. Systematic empirical results in global localization based on sonar illustrate superior performance, when compared to other state-of-the-art updating of Monte Carlo localization.

Most information feature extraction (MIFE) approach for face recognition

We present a MIFE (Most Information Feature Extraction) approach, which extract as abundant as possible information for the face classification task. In the MIFE approach, a facial image is separated into sub-regions and each sub-region makes individual’s contribution for performing face recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. Experiment results show that the proposed SadaGamma/SHE correction approach provides an efficient delighting solution for face recognition. MIFE and SadaGamma/SHE correction together achieves lower error ratio in face recognition under different illumination and expression.