Zengxi Huang

We develop a robust multimodal biometric system using Sparse Representation (SR), which integrates the face and ear at feature level, and can effectively adjust the fusion rule based on reliability difference between the modalities. Expand

We propose an adaptive face and ear based bimodal recognition framework using sparse coding, namely ABSRC, which can effectively reduce the adverse effect of degraded modality. Expand

We introduce three frameworks for multimodal biometric using sparse representation based classification (SRC), which has been successfully used in many classification tasks recently. Expand

This paper investigates the existence and stability of a discontinuous almost periodic solution of impulsive Hopfield neural networks with finite distributed delays and periodic impulses, using fixed point theorems, Lyapunov functional, and some inequality techniques. Expand

This paper presents an improved self-organization map (SOM) algorithm and its application to color feature extraction from images. Expand

In this paper, we propose a novel method called structure regularized sparse coding (SRSC) for the sparse representation of the data in the unsupervised scenario. Expand

We proposed a method based on fully convolutional neural network for human ear detection called FCNED. Expand

In this paper, we propose a multimodal verification system integrating face and ear based on sparse representation based classification (SRC). Expand

The support vector ordinal regression (SVOR) method is derived from support vector machine and developed to tackle the Ordinal regression problems. Expand

We use the collaborative representation fidelity with non-target subjects to measure the affinity of a query sample to the claimed client and train a support vector machine classifier. Expand