Guo-Can Feng

Human face recognition using PCA on wavelet subband

Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area since early 1990. Nowadays, principal component analysis (PCA) has been widely adopted as the most promising face recognition algorithm. Yet still, traditional PCA approach has its limitations: poor discrimi- natory power and large computational load. In view of these limita- tions, this article proposed a subband approach in using PCA— apply PCA on wavelet subband. Traditionally, to represent the human face, PCA is performed on the whole facial image. In the proposed method, wavelet transform is used to decompose an im- age into different frequency subbands, and a midrange frequency subband is used for PCA representation. In comparison with the traditional use of PCA, the proposed method gives better recogni- tion accuracy and discriminatory power; further, the proposed method reduces the computational load significantly when the im- age database is large, with more than 256 training images. This article details the design and implementation of the proposed method, and presents the encouraging experimental results.

Multi-cues eye detection on gray intensity image

Abstract This paper presents a novel eye detection method for gray intensity image. The precise eye position can be located if the eye windows are accurately detected. The proposed method uses multi-cues for detecting eye windows from a face image. Three cues from the face image are used. Each cue indicates the positions of the potential eye windows. The first cue is the face intensity because the intensity of eye regions is relatively low. The second cue is based on the estimated direction of the line joining the centers of the eyes. The third cue is from the response of convolving the proposed eye variance filter with the face image. Based on the three cues, a cross-validation process is performed. This process generates a list of possible eye window pairs. For each possible case, variance projection function is used for eye detection and verification. A face database from MIT AI laboratory, which contains 930 face images with different orientations and hairstyles captured from different people, is used to evaluate the proposed system. The detection accuracy is 92.5%.

Variance projection function and its application to eye detection for human face recognition

Abstract We present a new approach for eye detection using the variance projection function. The variance projection function is developed and employed to locate landmarks of the human eye which are then used to guide the detection of the eye position and shape. A number of eye images are selected to evaluate the capability of the proposed method and the results are encouraging.

Recognition of head-and-shoulder face image using virtual frontal-view image

This paper addresses the problem of face recognition under varying poses. To recognize a face under different poses, one approach is to use a human face 3D model. This approach is flexible but the equipment for acquiring the 3D face image is very expensive. The second approach is view-based. However, the complexity of the system is very high, as it requires constructing a representation for each view. For a 3D rotation, construction of dozens of representations may be required. This paper proposes a new idea to transform the face with unknown pose into frontal view for recognition. To construct the virtual frontal view image, we have developed an algorithm for detecting facial landmarks, which are then used to estimate the orientation of the face. A generic 3D spring-based face model is developed to transform the unknown face image into virtual frontal-view image. Finally, a spectroface method, which is based on wavelet transform and Fourier transform, is developed to recognize the virtual frontal face image. The proposed method has been tested by 1145 face images from 85 persons with different poses, facial expressions and small occlusions. The recognition accuracy for the best match is 84.7%. If we consider the top three matches, the accuracy increases to 92.9%.

A contour detection method: initialization and contour model

Abstract In this paper, a new contour detection method based on the snake model is developed and reported. The proposed method consists of two steps. The first step is to locate the initial snake contour and a novel initialization algorithm has been developed. In the second step, an improved snake algorithm is developed to locate the final contour(s). Images with single and multiple objects are selected to evaluate the capability of the proposed method and the results are encouraging.

Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions

Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem of semi-supervised metric learning. We propose a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraints. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness, cluster (low density) and manifold information point of view. The regularizer is then combined with the large margin hinge loss on the triplet constraints. In other words, we keep a large margin between different labeled samples, and in the meanwhile, we use the unlabeled samples to regularize it. Then the semi-supervised metric learning method is developed. We have performed experiments on classification using publicly available databases to evaluate the proposed method. To our best knowledge, this is the only method satisfying all the three semi-supervised assumptions, namely smoothness, cluster (low density) and manifold. Experimental results have shown that the proposed method outperforms state-of-the-art semi-supervised distance metric learning algorithms.

A novel method for parameter estimation of digital arc

A novel algorithm for parameter estimation of circular arcs is developed and reported in this letter. The proposed method is unbiased and consistent. Experimental results show that our method provides a better accuracy than certain existing methods.

Printed Chinese Character Similarity Measurement Using Ring Projection and Distance Transform

This paper presents a new Chinese character similarity measurement method based on the ring projection algorithm and distance transform. The ring projection algorithm is used to transform a character image with two independent variables into a function of one independent variable in the ring projection space. This representation of character in the ring projection space has been proved to be in orientation and scale invariant. However, this representation will be distorted nonlinearly in the presence of noise. Therefore, common linear metrics such as Euclidean distance, cannot be applied to measure distance. To solve the nonlinear distortion problem, distance transform is proposed as a nonlinear metric. The similarity measurement is performed using the distance transformed image in the ring projection space. A number of Chinese characters are selected to evaluate the capability of the proposed measurement scheme and the results are encouraging.

Learning Kernel in Kernel-Based LDA for Face Recognition Under Illumination Variations

Kernel-based methods have been proved to be an effective approach for face recognition in dealing with complex and nonlinear face image variations. While many encouraging results have been reported, the selection of kernel is rather ad hoc. This letter proposes a systematic method to construct a new kernel for kernel discriminant analysis, which is good for handling illumination problem. The proposed method first learns a kernel matrix by maximizing the difference between inter-class and intra-class similarities under the Lambertian model, and then generalizes the kernel matrix to our proposed ILLUM kernel using the scattered data interpolation technique. Experiments on the Yale-B and the CMU PIE face databases show that, the proposed kernel outperforms the popular Gaussian kernel in Kernel Discriminant Analysis and the recognition rate can be improved around 10%.

Virtual view face image synthesis using 3D spring-based face model from a single image

It is known that 2D views of a person can be synthesised if the face 3D model of that person is available. This paper proposes a new method, called 3D spring-based face model (SBFM), to determine the precise face model of a person with different poses and facial expressions from a single image. The SBFM combines the concepts of generic 3D face model in computer graphics and deformable template in computer vision. Face image databases from MIT AI laboratory and Yale University are used to test our proposed method and the results are encouraging.