In this paper, we propose a 3D face recognition approach based on the conformal representation of facial surfaces. Firstly, facial surfaces are mapped onto the 2D unit disk by Riemann mapping. Their conformal representation (i.e. the pair of mean curvature (MC) and conformal factor (CF) ) are then computed and encoded to Mean Curvature Images (MCIs) and Conformal Factor Images (CFIs). Considering that different regions of face deform unequally due to expression variation, MCIs and CFIs are divided into five parts. LDA is applied to each part to obtain the feature vector. At last, five parts are fused on the distance level for recognition. Extensive experiments carried out on the BU-3DFE database demonstrate the effectiveness of the proposed approach.
Different face views project different face topology in 2D images. The unified processing of face images with less topology different related to smaller range of face view angles is more convenient, and vice versa. Thus many researches divided the entire face pattern space form multiview face images into many subspaces with small range of view angles. However, large number of subspaces is computationally demanding, and different face processing algorithms take different strategies to handle the view changing. Therefore, the research of proper division of face pattern space is needed to ensure good performance. Different from other researches, this paper proposes an optimal view angle range criterion of face pattern space division in theory by careful analysis on the structure differences of multiview faces and on the influence to face processing algorithms. Then a face pattern space division method is proposed. Finally, this paper uses the proposed criterion and method to divide the face pattern space for face detection and compares with other division results. The final results show the proposed criterion and method can satisfy the processing performance with minimum number of subspace. The study in this paper can also help other researches which need to divide pattern space of other objects based on their different views.
In this paper, we introduce a novel Gabor based Spacial Domain Class-Dependence Feature Analysis(GSD-CFA)
method that increases the Face Recognition Grand Challenge (FRGC)2.0 performance. In short, we integrate
Gabor image representation and spacial domain Class-Dependence Feature Analysis(CFA) method to perform
fast and robust face recognition. In this paper, we mainly concentrate on the performances of subspace-based
methods using Gabor feature. As all the experiments in this study is based on large scale face recognition
problems, such as FRGC, we do not compare the algorithms addressing small sample number problem. We study
the generalization ability of GSD-CFA on THFaceID data set. As FRGC2.0 Experiment #4 is a benchmark test
for face recognition algorithms, we compare the performance of GSD-CFA with other famous subspace-based
algorithms in this test.
Eye blink detection is one of the important problems in computer vision. It has many applications such as face live
detection and driver fatigue analysis. The existing methods towards eye blink detection can be roughly divided into two
categories: contour template based and appearance based methods. The former one usually can extract eye contours
accurately. However, different templates should be involved for the closed and open eyes separately. These methods are
also sensitive to illumination changes. In the appearance based methods, image patches of open-eyes and closed-eyes are
collected as positive and negative samples to learn a classifier, but eye contours can not be accurately extracted. To
overcome drawbacks of the existing methods, this paper proposes an effective eye blink detection method based on an
improved eye contour extraction technique. In our method, eye contour model is represented by 16 landmarks therefore
it can describe both open and closed eyes. Each landmark is accurately recognized by fast classifier which is trained from
the appearance around this landmark. Experiments have been conducted on YALE and another large data set consisting
of frontal face images to extract the eye contour. The experimental results show that the proposed method is capable of
affording accurate eye location and robust in closed eye condition. It also performs well in the case of illumination
variants. The average time cost of our method is about 140ms on Pentium IV 2.8GHz PC 1G RAM, which satisfies the
real-time requirement for face video sequences. This method is also applied in a face live detection system and the
results are promising.
Handwritten and machine-printed characters are recognized separately in most OCR systems due to their distinct difference. In applications where both kinds of characters are involved, it is necessary to judge a character’s handwritten/printed property before feeding it into the proper recognition engine. In this paper, a new method to discriminate between handwritten and machine-printed character is proposed. Unlike most previous works, the discrimination we carried out in this paper is totally based on single character. Five kinds of statistical features are extracted from character image, then feature selection and classification are implemented simultaneously by a learning algorithm based on AdaBoost. Experiments on large data sets have demonstrated the effectiveness of the method.
The difficulties of handwritten numeral recognition mainly result from the intrinsic deformations of the handwritten numeral samples. One way to tackle these difficulties is to describe the variations of the feature vectors belonging to one class. Subspace method is a well-known effective pattern recognition method to fulfill this idea. In fact, the subspace method can be embedded into a multivariate linear regression model which response variables are the feature vector and the predictor variables are the principal components (PCs) of the feature vector. When the feature vector is not exactly a Gaussian distribution, it is possible to describe the feature vector more accurately in the sense of least mean squares (LMS) by some nonlinear functions parameterized by the same PCs. This method may result in a higher recognition performance. In this paper we propose an algorithm based on multivariate polynomial regression to fulfill this nonlinear extension. We use the projection pursuit regression (PPR) to determine the multivariate polynomials, in which the polynomial degrees are selected by the structural risk minimization (SRM) method. Experimental results show that our approach is an effective pattern recognition method for the problem of handwritten numeral recognition.