Xiao-Yuan Jing

A face and palmprint recognition approach based on discriminant DCT feature extraction

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

Letters: Face and palmprint feature level fusion for single sample biometrics recognition

In the application of biometrics authentication (BA) technologies, the biometric data usually shows three characteristics: large numbers of individuals, small sample size and high dimensionality. One of major research difficulties of BA is the single sample biometrics recognition problem. We often face this problem in real-world applications. It may lead to bad recognition result. To solve this problem, we present a novel approach based on feature level biometrics fusion. We combine two kinds of biometrics: one is the face feature which is a representative of contactless biometrics, and another is the palmprint feature which is a typical contact biometrics. We extract the discriminant feature using Gabor-based image preprocessing and principal component analysis (PCA) techniques. And then design a distance-based separability weighting strategy to conduct feature level fusion. Using a large face database and a large palmprint database as the test data, the experimental results show that the presented approach significantly improves the recognition effect of single sample biometrics problem, and there is strong supplement between face and palmprint biometrics.

Rapid and brief communications: A Fourier-LDA approach for image recognition

Fourier transform and linear discrimination analysis (LDA) are two commonly used techniques of image processing and recognition. Based on them, we propose a Fourier-LDA approach (FLA) for image recognition. It selects appropriate Fourier frequency bands with favorable linear separability by using a two-dimensional separability judgment. Then it extracts two-dimensional linear discriminative features to perform the classification. Our experimental results on different image data prove that FLA obtains better classification performance than other linear discrimination methods.

An improved LDA approach

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.

Face recognition based on discriminant fractional Fourier feature extraction

Developed from the conventional Fourier transform, the fractional Fourier transform is a powerful signal analysis and processing technique. In this paper, we apply it to the field of face recognition. By combining it with the discrimination analysis technique, we propose a new face recognition approach. First, we use a two-dimensional separability judgment to select appropriate value of angle parameter for discrete fractional Fourier transform. Second, we present a reformative Fisherface method to extract discriminative features from the preprocessed images and perform the classification using the nearest neighbor classifier. Experimental results on two public face databases indicate that our approach outperforms four representative discrimination methods. It obtains better and robust classification effects.

Improvements on the linear discrimination technique with application to face recognition

In this paper, new improvements for the linear discrimination technique are proposed. These improvements include effective solutions for the small sample size problem, the selection of appropriate principal component and more accurate within-class scatter estimation for the Fisher criterion. The effectiveness of our approach is proved by experimental results on the Yale face database.

UODV: improved algorithm and generalized theory

Uncorrelated optimal discrimination vectors (UODV) is an effective linear discrimination approach. However, this approach has the disadvantages in both the algorithm and the theory. In light of this, we propose an improved UODV algorithm based on the typical principal component analysis (TPCA), which can satisfy the statistical uncorrelation and utilize the total scatter information of the training samples. Then, a new and generalized theorem on UODV is presented. This generalized theorem reveals the essential relationship between UODV and the well-known Fisherface method, and proves that our improved UODV algorithm is theoretically superior to the Fisherface method. Experimental results on both 1-D and 2-D data prove that our algorithm outperforms the original UODV approach and the Fisherface method

Improvements on the uncorrelated optimal discriminant vectors

The algorithm and the theorem of uncorrelated optimal discriminant vectors (UODV) were proposed by Jin. In this paper, we present new improvements to Jins method, which include an improved approach for the original algorithm and a generalized theorem for UODV. Experimental results prove that our approach is superior to the original in the recognition rate.

Face recognition based on a group decision-making combination approach

Abstract This paper proposes a novel and real-time classifiers combination approach, group decision-making combination (GDC) approach, which can dynamically select the classifiers and perform linear combination. We also prove that the orthogonal wavelet transform can be regarded as an effective images preprocessing tool adapted to classifiers combination. GDC has been successfully used for face recognition, which can improve on the recognition rate for the algebraic features. Experiment results also show that it is superior to the conventional combination method, majority voting method.

Face recognition based on linear classifiers combination

Abstract In this paper, an approach that uses a combination of linear classifiers is applied to face recognition. We propose a novel criterion for the combination, the maximum complementariness criterion, which is used to construct the fitness function for a genetic algorithm (GA). A GA is then used to generate the rational weights for the classifiers. Experiments show that our approach can successfully improve the performance of the classification for face recognition using the commonly used Fisherface features.