Lijun Yan

Adaptively weighted sub-directional two-dimensional linear discriminant analysis for face recognition

A novel image classification algorithm named Adaptively Weighted Sub-directional Two-Dimensional Linear Discriminant Analysis (AWS2DLDA) is proposed in this paper. AWS2DLDA can extract the directional features of images in the frequency domain, and it is applied to face recognition. Some experiments are conducted to demonstrate the effectiveness of the proposed method. Experimental results confirm that the recognition rate of the proposed system is higher than the other popular algorithms.

Generalized Discrete Fractional Hadamard Transformation and its Application on the Image Encryption

Discrete fractional Hadamard transform (DFHaT) is a generalization of discrete Hadamard transform. It has been widely used in signal processing. Different from only having one order parameter, in this paper, a new generalized discrete fractional Hadamard transform (GDFHaT) is proposed, which has multi-order parameters. This kind of GDFHaT shares most properties with DFHaT. The GDFHaT becomes DFHaT when all of its order parameters are the same. Then we apply it to the image encryption. Results show this method performs well.

Discriminant analysis based on nearest feature line

A novel feature extraction algorithm based on nearest feature line is proposed in this paper. The proposed algorithm can extract the local discriminant features of the samples. The performance of the proposed algorithm is directly associated with the parameter, so we use two discriminant power criterions to adaptively determine the parameter. Some experiments are implemented to evaluate the proposed algorithm and the experimental results demonstrate the efficiency of the proposed algorithm.

Data Hiding Based on Subsampling and Compressive Sensing

This paper proposes a novel scheme that considers the data hiding with sub sampling and compressive sensing. We utilize the characteristics of compressive sensing, sparsity and random projection, to embed secret data in the observation domain of the sparse image obtained through compressive sensing. The high bit correction rate (BCR) in experiments shows the high accuracy of our proposed method.

Directional two-dimensional principal component analysis for face recognition

In this paper, two novel face recognition frames are proposed, called single directional two dimensional principal component analysis (SD2DPCA) and multi-directional two dimensional principal component analysis (MD2DPCA). Compared with other popular algorithms, SD2DPCA needs less running time while achieves almost the same correct recognition rate. MD2DPCA can extract the directional feature of face images more efficiently, so it gets a higher recognition rate, and experimental results demonstrate that the SD2DPCA and MD2DPCA have their advantages.

A Novel Brain-Computer Interface Controller Scheme for Multimedia Control

Due to the revolutionary developments in computer science and electronics, multiple kinds of new controllers are introduced. Such as eye tracking, voice recognition and hand gesture recognition. But these controller scheme still have their drawbacks. In this paper, we proposed an improved brain-computer interface (BCI) controller based on SSVEP. Our scheme provide good accuracy and comfortable user experience.

Neighborhood Discriminant Nearest Feature Line Analysis and Its Application to Face Recognition

In this paper, a novel subspace learning algorithm, called neighborhood discriminant nearest feature line analysis (NDNFLA), is proposed. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) scatter and minimizing the within-class FL scatter. At the same time, the neighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.

Feature Line-Based Local Discriminant Analysis for Image Feature Extraction

In this paper, a novel image feature extraction algorithm, entitled Feature Line-based Local Discriminant Analysis (FLLDA), is proposed. FLLDA is a subspace learning algorithm based on Feature Line (FL) metric. FL metric is used for the evaluation of the local within-class scatter and local between class scatter in the proposed FLLDA approach. The Experimental results on COIL20 image database confirm the effectiveness of the proposed algorithm.

Feature Extraction Based on Nearest Feature Line and Compressive Sensing

In this paper, a novel feature extraction algorithm based on nearest feature line and compressive sensing is proposed. The prototype samples are transformed to compressive sensing domain and then are performed Neighborhood discriminant nearest feature line analysis (NDNFLA) in the proposed algorithm. This method can reduce the computational complexity for feature extraction using nearest feature line. At the same time.its average recognition rate is very close to that of NDNFLA. The proposed algorithm is applied to image classification on AR face Database. The experimental results demonstrate the effectiveness of the proposed algorithm.

Directional discriminant analysis based on nearest feature line

In this paper, two novel image feature extraction algorithms based on directional filter banks and nearest feature line are proposed, which are named Single Directional Feature Line Discriminant Analysis (SD-NFDA) and Multiple Directional Feature Discriminant Line Analysis (MD-NFDA). SD-NFDA and MD-NFDA extract not only the statistic feature of samples, but also the directionality feature. SD-NFDA and MD-NFDA can get higher average recognition rate with less running time than other nearest feature line based feature extraction algorithms. Experimental results confirm the advantages of SD-NFDA and MD-NFDA.