Biomedical subjects

Haihong Zhang

Publications and source records attributed to Haihong Zhang.

4 recordsLinked to original sources

PubMed2005

Gabor wavelet associative memory for face recognition.

This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.

Algorithms↗

PubMed2005

A kernel autoassociator approach to pattern classification.

Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.

Algorithms↗

PubMed2004

Face recognition by applying wavelet subband representation and kernel associative memory.

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Association Learning↗

PubMed2002

[Retroviral vector-mediated HBsAg expression and its stability].

OBJECTIVE: To explore use of retroviral vector in gene therapy of hepatitis B. METHODS: The recombinant vector Plxsn-HBs was constructed by inserting HBV S gene into pLXSN. The pseudovirus, which was produced from PA317 after transferring with pLXSN-HBs by electroporation, were frozen at different temperature. The activities of the pseudovirus to infect eukaryotic cells and express antigen were determined by comparing the numbers of G418-resistant clones and assaying HBsAg in the supernatant of the cells with ELISA after infection HepG2, NIH3T3 and 293 cells. RESULTS: It was hard to find changes in HBsAg amount at different intervals and different temperatures. G418 resistant clones, however, were variable. When frozen at -20 degrees C, the numbers of clones were half less than that of the beginning after 6 months, few clones were formed after 12 months, and no clone was found after 24 months. When frozen at -40 degrees C, the numbers of clones were 121, 332 and 89 42, 137 and 43 for HepG2, NIH3T3 and 293 cell lines at 12 and 24 months, respectively. When frozen at -70 degrees C, the numbers of clones were 159 463 and 112 for HepG2, NIH3T3 and 293 cell lines at 24 months, respectively. There was no statistical difference compared to that of zero months. CONCLUSIONS: The activity of the peseudovirus to infect eukaryotic cells and expressed antigen was not changed after 2 years frozen at -70 degrees C.

Cell Line↗