Wei Jia

Palmprint Verification Based on Robust Orientation Code

In palmprint recognition field, orientation based approaches are thought to achieve the best results in terms of recognition rates. In this paper, we propose a novel orientation based scheme, in which three strategies, the modified finite Radon transform, enlarged training set and pixel to area matching, have been designed to further improve its performance. The experimental results of verification conducted on Hong Kong Polytechnic University Palmprint Database show that our approach has higher recognition rates and faster processing speed.

Palmprint verification based on principal lines

In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based on pixel-to-area comparison is devised to calculate the similarity between two palmprints, which has shown good robustness for slight rotations and translations of palmprints. The experimental results for the verification on Hong Kong Polytechnic University Palmprint Database show that the discriminability of principal lines is also strong.

Discriminant sparse neighborhood preserving embedding for face recognition

Sparse subspace learning has drawn more and more attentions recently. However, most of the sparse subspace learning methods are unsupervised and unsuitable for classification tasks. In this paper, a new sparse subspace learning algorithm called discriminant sparse neighborhood preserving embedding (DSNPE) is proposed by adding the discriminant information into sparse neighborhood preserving embedding (SNPE). DSNPE not only preserves the sparse reconstructive relationship of SNPE, but also sufficiently utilizes the global discriminant structures from the following two aspects: (1) maximum margin criterion (MMC) is added into the objective function of DSNPE; (2) only the training samples with the same label as the current sample are used to compute the sparse reconstructive relationship. Extensive experiments on three face image datasets (Yale, Extended Yale B and AR) demonstrate the effectiveness of the proposed DSNPE method.

Completed Local Binary Count for Rotation Invariant Texture Classification

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.

Completed robust local binary pattern for texture classification

Original Local Binary Pattern (LBP) descriptor has two obvious demerits, i.e., it is sensitive to noise, and sometimes it tends to characterize different structural patterns with the same binary code which will reduce its discriminability inevitably. In order to overcome these two demerits, this paper proposes a robust framework of LBP, named Completed Robust Local Binary Pattern (CRLBP), in which the value of each center pixel in a 3x3 local area is replaced by its average local gray level. Compared to the center gray value, average local gray level is more robust to noise and illumination variants. To make CRLBP more robust and stable, Weighted Local Gray Level (WLG) is introduced to take place of the traditional gray value of the center pixel. The experimental results obtained from four representative texture databases show that the proposed method is robust to noise and can achieve impressive classification accuracy.

Locality preserving discriminant projections for face and palmprint recognition

A new subspace learning algorithm called locality preserving discriminant projections (LPDP) is proposed by adding the criterion of maximum margin criterion (MMC) into the objective function of locality preserving projections (LPP). LPDP retains the locality preserving characteristic of LPP and utilizes the global discriminative structures obtained from MMC, which can maximize the between-class distance and minimize the within-class distance. Thus, our proposed LPDP combining manifold criterion and Fisher criterion has more discriminanting power, and is more suitable for recognition tasks than LPP, which considers only the local information for classification tasks. Moreover, two kinds of tensorized (multilinear) forms of LPDP are also derived in this paper. One is iterative while the other is non-iterative. The proposed LPDP method is applied to face and palmprint biometrics and is examined using the Yale and ORL face image databases, as well as the PolyU palmprint database. Experimental results demonstrate the effectiveness of the proposed LPDP method.

Multiscale Distance Matrix for Fast Plant Leaf Recognition

In this brief, we propose a novel contour-based shape descriptor, called the multiscale distance matrix, to capture the shape geometry while being invariant to translation, rotation, scaling, and bilateral symmetry. The descriptor is further combined with a dimensionality reduction to improve its discriminative power. The proposed method avoids the time-consuming pointwise matching encountered in most of the previously used shape recognition algorithms. It is therefore fast and suitable for real-time applications. We applied the proposed method to the task of plan leaf recognition with experiments on two data sets, the Swedish Leaf data set and the ICL Leaf data set. The experimental results clearly demonstrate the effectiveness and efficiency of the proposed descriptor.

Histogram of Oriented Lines for Palmprint Recognition

Subspace learning methods are very sensitive to the illumination, translation, and rotation variances in image recognition. Thus, they have not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named histogram of oriented lines (HOL), which is a variant of histogram of oriented gradients (HOG). HOL is not very sensitive to changes of illumination, and has the robustness against small transformations because slight translations and rotations make small histogram value changes. Based on HOL, even some simple subspace learning methods can achieve high recognition rates.

Letters: Palmprint recognition with 2DPCA+PCA based on modular neural networks

In this letter, a novel modular neural network (MNN) classifier, which partitions a K-class problem into many much easier two-class problems in sub-subspaces, was proposed to perform palmprint recognition. Moreover, in order to make palmprint recognition more accurate, we introduced 2DPCA technique into the extraction of palmprint features, and removed the illumination information from the collected palm images using w/o3 technique. Our approach was compared with several existing methods, and obtained a satisfying classification performance on the Hong Kong Polytechnic University (PolyU) Palmprint Database.

An Intensity-Texture model based level set method for image segmentation

In this paper, a novel level set segmentation model integrating the intensity and texture terms is proposed to segment complicated two-phase nature images. Firstly, an intensity term based on the global division algorithm is proposed, which can better capture intensity information of image than the Chan-Vese model (CV). Particularly, the CV model is a special case of the proposed intensity term under a certain condition. Secondly, a texture term based on the adaptive scale local variation degree (ASLVD) algorithm is proposed. The ASLVD algorithm adaptively incorporates the amplitude and frequency components of local intensity variation, thus, it can extract the non-stationary texture feature accurately. Finally, the intensity term and the texture term are jointly incorporated into level set and used to construct effective image segmentation model named as the Intensity-Texture model. Since the intensity term and the texture term are complementary for image segmentation, the Intensity-Texture model has strong ability to accurately segment those complicated two-phase nature images. Experimental results demonstrate the effectiveness of the proposed Intensity-Texture model. An intensity term based on the so-called global division algorithm is proposed.We extract the amplitude and frequency components of local intensity variation.We propose the adaptive scale local variation degree algorithm as texture term.The intensity and texture terms are integrated into level set energy functional.