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This paper proposes an approach to fingerprint binarization integrating nonlinear diffusion filtering scheme and topographic scheme in which the properties of essential flow-like patterns of fingerprint are deliberately analyzed in different points of view. The filtering scheme is on the basis of the the coherent structures, while the topographic scheme is based on the analysis of the underlying 2D surface. The fingerprint image is smoothed along the coherent structures and binarized according to the sign of the trace of the Hessian matrix. The integration method is tested with a series of experiments and the results reveal the good performance of our algorithm. [ABSTRACT FROM AUTHOR]

The detection of singular points(core and delta) is very important for fingerprint classification and some fingerprint matching based on reference points. This paper propose a new algorithm for extracting the singular points using the combination of curvature and orientation field of fingerprint images, which provide a more accurate description of characteristic of singular points. The effectiveness of our algorithm has been shown by the experiments implemented in the public databases, DB2 and DB3, FVC2002. [ABSTRACT FROM AUTHOR]

Boosting is a general method for improving the performance of any learning algorithm. In this paper, an unusual regularization technique, boosting in random subspace, is employed to improve generalization capability of boosting. Meanwhile the space complexity of training is lowered, and the classifier combination strategy can be used to further improve recognition accuracy. Using the method, we achieved 98.99% rank-1 recognition rate on FERET fb probe set. [ABSTRACT FROM AUTHOR]

Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual information into AdaBoost, we propose an improved boosting algorithm in this paper. The proposed method fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected are both accurate and non-redundant. Experimental results show that the strong classifier learned using the proposed algorithm achieves a lower training error rate than AdaBoost. The proposed algorithm has also been applied to select discriminative Gabor features for face recognition. Even with the simple correlation distance measure and 1-NN classifier, the selected Gabor features achieve quite high recognition accuracy on the FERET database, where both expression and illumination variance exists. When only 140 features are used, the selected features achieve as high as 95.5% accuracy, which is about 2.5% higher than that of features selected by AdaBoost. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian network based multi-cue fusion method for robust and real-time fist tracking. Firstly, a new strategy, which employs the latest work in face recognition, is used to create accurate color model of the fist automatically. Secondly, color cue and motion cue are used to generate the possible position of the fist. Then, the posterior probability of each possible position is evaluated by Bayesian network, which fuses color cue and appearance cue. Finally, the fist position is approximated by the hypothesis that maximizes a posterior. Experimental results show that our algorithm is real-time and robust. [ABSTRACT FROM AUTHOR]

It has been demonstrated that combining the decisions of several classifiers can lead to better recognition results. The combination can be implemented using a variety of schemes, among which voting method is the simplest, but it has been found to be just as effective as more complicated strategies in improving the recognition results. In this paper, we propose a voting method for object location, which can be viewed as generalization of majority vote rule. Using this method, we locate eye centers in face region. The experimental results demonstrate that the locating performance is comparable with other newly proposed eye locating methods. The voting method can be considered as a general fusion scheme for precise location of object. [ABSTRACT FROM AUTHOR]

Shadows produce troublesome effects in many computer vision applications. The idea behind most current shadow removal approaches is locating shadows and then removing them[1][4]. However, distinguishing shadow edges due to shadows from reflectance edges due to reflectance changes is a difficult problem, particularly in a single image. In this paper, we focus on the shadow removal problem in face recognition, and take a novel method based on ICA (Independent Component Analysis) to remove shadows from a single face images. The training set contains face images without shadows. Firstly, we applied derivative filters on training images to derive face edge maps, and then perform ICA on filtered training set to construct pixel ICA subspaces which can be used to remove shadow edges from the filtered versions of a single test image. After the shadow edges removal process, a shadow free image can be reconstructed using an approach similar to [7]. Unlike previous shadow removal approaches, our method can remove shadows from a single gray image. Experimental results demonstrate that the proposed approach can effectively eliminate the effects of shadows in face recognition. [ABSTRACT FROM AUTHOR]

In this work, we present a novel approach for face recognition which use boosted statistical local Gabor feature based classifiers. Firstly, two Gabor parts, real part and imaginary part, are extracted for each pixel of face images. The two parts are transformed into two kinds of Gabor features, magnitude feature and phase feature. 40 magnitude Gaborfaces and 40 phase Gaborfaces are generated for each face image by convoluting face images with five scales and eight orientations Gabor filters. Then these Gaborfaces are scanned with a sub-window from which the quantified Gabor features histograms are extracted representing efficiently the face image. The multi-class problem of face recognition is transformed into a two-class one of intra-and extra-class classification using intra-personal and extra-personal images, as in [5]. The intra/extra features are constructed based on these histograms of two different face images with Chi square statistic as dissimilarity measure. A strong classifier is learned using boosting examples, similar to the way in face detection framework [10]. Experiments on FERET database show good results comparable to the best one reported in literature [6]. [ABSTRACT FROM AUTHOR]