Vision-based motion estimation is a key technique in autonomously mobile robot. This paper presen... more Vision-based motion estimation is a key technique in autonomously mobile robot. This paper presents an integrated method for motion estimation of the mobile robot based on floor plane extraction. The whole processes include floor plane extraction, pose estimation of the onboard stereo vision system and motion estimation of the mobile robot. First, based on disparity map obtained from the onboard
Constraint Shape Model is proposed to extract facial feature using two different search methods f... more Constraint Shape Model is proposed to extract facial feature using two different search methods for contour points and control points individually. In the proposed algorithm, salient facial features, such as the eyes and the mouth, are first localized and utilized to initialize the shape model and provide region constraints on iterative shape searching. For the landmarks on the face contour, the edge intensity is exploited to construct better local texture matching models. Moreover, for control points, the proposed Gabor wavelet based method is used to search it by multi-frequency strategy. To test the proposed approaches, on a database containing 500 labeled face images, experiments are conducted, which shows that the proposed method performs significantly better in terms of a deliberate performance evaluation method. The proposed method can be easily used to other texture objects, which is robust to variations in illumination and facial expression.
Kernel based methods have been of wide concern in the field of machine learning. This paper propo... more Kernel based methods have been of wide concern in the field of machine learning. This paper proposes a novel Gabor-Kernel Fisher analysis method (G-EKFM) for face recognition, which applies Enhanced Kernel Fisher Model (EKFM) on Gaborfaces derived from Gabor wavelet representation of face images. We show that the EKFM outperforms the Generalized Kernel Fisher Analysis (GKFD) model. The performance of G-EKFM is evaluated on a subset of FERET database and CAS-PEAL database by comparing with various face recognition schemes, such as Eigenface, GKFA, Image-based EKFM, Gabor-based GKFA, and so on.
A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face r... more A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate
This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two app... more This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two approaches are applied for neighborhood construction, called k-neighbor and ε-neighbor. For the k-neighbor, the number of the neighbors is a const k. For the other one, all the distances between the neighbors is less than ε. Either the k-neighbor or the ε-neighbor neglects the difference of each point. This paper proposes an new method called the kc-neighbor, in which the neighbors are defined based on c time distance of the k nearest neighbor, which can avoid the neighborhood graph unconnected and improve the accuracy in computing neighbors. In this paper, SVM rather than MDS is applied to classify head poses after the geodesic distances are computed. The experiments show the effectiveness of the proposed method.
This paper gives fair comparisons of shape and texture based methods for vein recognition. The sh... more This paper gives fair comparisons of shape and texture based methods for vein recognition. The shape of the back of hand contains information that is capable of authenticating the identity of an individual. In this paper, two kinds of shape matching method are used, which are based on Hausdorff distance and Line Edge Mapping(LEM) methods. The vein image also contains valuable texture information, and Gabor wavelet is exploited to extract the discriminative feature. In order to evaluate the system performance, a dataset of 100 persons of different ages above 16 and of different gender, each has 5 images per person is used. Experimental results show that Hausdorff, LEM and Gabor based methods achieved 58%, 66%, 80% individually.
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum A... more This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Machine with the Kernel Fisher Discriminant Analysis (KFD). We also use a simple method to prove the relationship between both kernel methods. The difference of KMAMC from traditional KFD methods include (1) the within-class and between-class scatter matrices are computed based on the support vectors instead of all the samples; (2) multiple centers are exploited instead of the single center in computing the two scatter matrices; (3) the discriminant criteria is formulated as subtracting the trace of within-class scatter matrix from that of the between-class scatter matrix, therefore, the tedious singularity problem is avoided. These features have made KMAMC more practical for real-world applications. Our experiments on two face databases, the FERET and CAS-PEAL face database, have illustrated its excellent performance compared with some traditional methods such as Eigenface, Fisherface, and KFD.
A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is ... more A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vector of the optimal Decision Boundary in terms of the Structure Risk Minimization principle. We also use a simple method to prove a property of Support Vector Machine (SVM) algorithm, which is combined with the optimal Decision Boundary Feature matrix to make our method consistent with the Kernel Fisher method(KFD). Moreover, KDBA is easily used in its applications, and the traditional Decision Boundary Analysis implementations are computationally expensive and sensitive to the size of the problem. Text classification problem is first used to testify the effectiveness of KDBA. Then experiments on the large-scale face database, the CAS-PEAL database, have illustrated its excellent performance compared with some popular face recognition methods such as Eigenface, Fisherface, and KFD.
IEEE Transactions on Circuits and Systems for Video Technology, 2011
This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for back... more This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.
Eurasip Journal on Advances in Signal Processing, 2008
This paper proposes a new face recognition method, named kernel learning of histogram of local Ga... more This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman's method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.
Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) c... more Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) challenge many traditional background subtraction methods. In this paper, we present a novel background subtraction approach for dynamic scenes, in which the background is modeled in a multi-resolution framework. First, for each level of the pyramid, we run an independent mixture of Gaussians models (GMM) that outputs a background subtraction map. Second, these background subtraction maps are combined via AND operator to finally get a more robust and accurate background subtraction map. This is a natural fusion because the original resolution and low resolution images have complementary strengths, which original resolution image contains rich information and low resolution image is insensitive to the noises and the small movement of dynamic scene. Experimental result shows that this real-time algorithm is able to detect moving objects accurately even in dynamic scenes.
Feature extraction and classification using Gabor wavelets have proven to be successful in comput... more Feature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.
In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and mult... more In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping method to capture the structure information in faces. Experiments on two face databases, FRGC Version 1 and FRGC Version 2, are conducted to compare the performances of the Gabor and GGabor features, which show that GGabor can also be a powerful tool to model faces, and the Efficient Kernel Fisher classifier can improve the efficiency of the original kernel fisher method.
Feature extraction, discriminant analysis, and classification rule are three crucial issues for f... more Feature extraction, discriminant analysis, and classification rule are three crucial issues for face recognition. This paper presents one method, named GaborfaceSVM, to handle three issues together. For feature extraction, we utilize the Gabor wavelet transform on grey face image to extract Gaborfaces. A Modified Enhanced Fisher Discriminant model is used to reinforce discriminant power of Gaborfaces. During classification process, Support Vector Machines classifier is proposed for robust decision in presence of wide facial and illumination variations. In experiments, the discriminant Gaborfaces approach incorporated with SVMs classifier demonstrates better effectiveness and performance than other methods.
Kernel Fisher Discriminant Analysis (KFDA) has achieved great success in pattern recognition rece... more Kernel Fisher Discriminant Analysis (KFDA) has achieved great success in pattern recognition recently. However, the training process of KFDA is too time consuming (even intractable) for a large training set, because, for a training set with n examples, both its between-class and within-class scatter matrices are of n n × and the time complexity of the KFDA training process is of 3 ( ) O n . Aiming at this problem, this paper employs Bagging technique to decrease the time-space cost of KFDA training process. In addition, this paper is more than just a simple application of Bagging. We have made an important adaptation which can further guarantee the performance of KFDA. Our experimental results demonstrate that the proposed method can not only greatly reduce the cost of time of the training process, but also achieve higher recognition accuracy than traditional KFDA and the simple application of Bagging.
This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), ... more This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The -order LDP is proposed to encode the ( 1) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP).
In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the hi... more In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the histogram of oriented gradient (HOG) features, a set of multi-scale orientation (MSO) features are proposed as the feature representation. The features are extracted on square image blocks of various sizes (called units), containing coarse and fine features in which coarse ones are the unit orientations and fine ones are the pixel orientation histograms of the unit. A cascade of Adaboost is employed to train classifiers on the coarse features, aiming to high detection speed. A greedy searching algorithm is employed to select fine features, which are input into SVMs to train the fine classifiers, aiming to high detection accuracy. Experiments report that our approach obtains state-of-art results with 12.4 times faster than the SVM+HOG method.
In recent years, sparse representation originating from signal compressed sensing theory has attr... more In recent years, sparse representation originating from signal compressed sensing theory has attracted increasing interest in computer vision research community. However, to our best knowledge, no previous work utilizes L1-norm minimization for human detection. In this paper we develop a novel human detection system based on L1-norm Minimization Learning (LML) method. The method is on the observation that a human object can be represented by a few features from a large feature set (sparse representation). And the sparse representation can be learned from the training samples by exploiting the L1-norm Minimization principle, which can also be called feature selection procedure. This procedure enables the feature representation more concise and more adaptive to object occlusion and deformation. After that a classifier is constructed by linearly weighting features and comparing the result with a calculated threshold. Experiments on two datasets validate the effectiveness and efficiency of the proposed method.
Journal of Visual Communication and Image Representation, 2011
This paper proposes a high-order Texture Pattern Flow (TPF) for complex background modeling and m... more This paper proposes a high-order Texture Pattern Flow (TPF) for complex background modeling and motion detection. The pattern flow is proposed to encode the binary pattern changes among the neighborhoods in the space–time domain. To model the distribution of the TPF pattern flow, the TPF integral histograms are used to extract the discriminative features to represent the input video. The
Vision-based motion estimation is a key technique in autonomously mobile robot. This paper presen... more Vision-based motion estimation is a key technique in autonomously mobile robot. This paper presents an integrated method for motion estimation of the mobile robot based on floor plane extraction. The whole processes include floor plane extraction, pose estimation of the onboard stereo vision system and motion estimation of the mobile robot. First, based on disparity map obtained from the onboard
Constraint Shape Model is proposed to extract facial feature using two different search methods f... more Constraint Shape Model is proposed to extract facial feature using two different search methods for contour points and control points individually. In the proposed algorithm, salient facial features, such as the eyes and the mouth, are first localized and utilized to initialize the shape model and provide region constraints on iterative shape searching. For the landmarks on the face contour, the edge intensity is exploited to construct better local texture matching models. Moreover, for control points, the proposed Gabor wavelet based method is used to search it by multi-frequency strategy. To test the proposed approaches, on a database containing 500 labeled face images, experiments are conducted, which shows that the proposed method performs significantly better in terms of a deliberate performance evaluation method. The proposed method can be easily used to other texture objects, which is robust to variations in illumination and facial expression.
Kernel based methods have been of wide concern in the field of machine learning. This paper propo... more Kernel based methods have been of wide concern in the field of machine learning. This paper proposes a novel Gabor-Kernel Fisher analysis method (G-EKFM) for face recognition, which applies Enhanced Kernel Fisher Model (EKFM) on Gaborfaces derived from Gabor wavelet representation of face images. We show that the EKFM outperforms the Generalized Kernel Fisher Analysis (GKFD) model. The performance of G-EKFM is evaluated on a subset of FERET database and CAS-PEAL database by comparing with various face recognition schemes, such as Eigenface, GKFA, Image-based EKFM, Gabor-based GKFA, and so on.
A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face r... more A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate
This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two app... more This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two approaches are applied for neighborhood construction, called k-neighbor and ε-neighbor. For the k-neighbor, the number of the neighbors is a const k. For the other one, all the distances between the neighbors is less than ε. Either the k-neighbor or the ε-neighbor neglects the difference of each point. This paper proposes an new method called the kc-neighbor, in which the neighbors are defined based on c time distance of the k nearest neighbor, which can avoid the neighborhood graph unconnected and improve the accuracy in computing neighbors. In this paper, SVM rather than MDS is applied to classify head poses after the geodesic distances are computed. The experiments show the effectiveness of the proposed method.
This paper gives fair comparisons of shape and texture based methods for vein recognition. The sh... more This paper gives fair comparisons of shape and texture based methods for vein recognition. The shape of the back of hand contains information that is capable of authenticating the identity of an individual. In this paper, two kinds of shape matching method are used, which are based on Hausdorff distance and Line Edge Mapping(LEM) methods. The vein image also contains valuable texture information, and Gabor wavelet is exploited to extract the discriminative feature. In order to evaluate the system performance, a dataset of 100 persons of different ages above 16 and of different gender, each has 5 images per person is used. Experimental results show that Hausdorff, LEM and Gabor based methods achieved 58%, 66%, 80% individually.
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum A... more This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Machine with the Kernel Fisher Discriminant Analysis (KFD). We also use a simple method to prove the relationship between both kernel methods. The difference of KMAMC from traditional KFD methods include (1) the within-class and between-class scatter matrices are computed based on the support vectors instead of all the samples; (2) multiple centers are exploited instead of the single center in computing the two scatter matrices; (3) the discriminant criteria is formulated as subtracting the trace of within-class scatter matrix from that of the between-class scatter matrix, therefore, the tedious singularity problem is avoided. These features have made KMAMC more practical for real-world applications. Our experiments on two face databases, the FERET and CAS-PEAL face database, have illustrated its excellent performance compared with some traditional methods such as Eigenface, Fisherface, and KFD.
A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is ... more A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vector of the optimal Decision Boundary in terms of the Structure Risk Minimization principle. We also use a simple method to prove a property of Support Vector Machine (SVM) algorithm, which is combined with the optimal Decision Boundary Feature matrix to make our method consistent with the Kernel Fisher method(KFD). Moreover, KDBA is easily used in its applications, and the traditional Decision Boundary Analysis implementations are computationally expensive and sensitive to the size of the problem. Text classification problem is first used to testify the effectiveness of KDBA. Then experiments on the large-scale face database, the CAS-PEAL database, have illustrated its excellent performance compared with some popular face recognition methods such as Eigenface, Fisherface, and KFD.
IEEE Transactions on Circuits and Systems for Video Technology, 2011
This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for back... more This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.
Eurasip Journal on Advances in Signal Processing, 2008
This paper proposes a new face recognition method, named kernel learning of histogram of local Ga... more This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman's method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.
Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) c... more Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) challenge many traditional background subtraction methods. In this paper, we present a novel background subtraction approach for dynamic scenes, in which the background is modeled in a multi-resolution framework. First, for each level of the pyramid, we run an independent mixture of Gaussians models (GMM) that outputs a background subtraction map. Second, these background subtraction maps are combined via AND operator to finally get a more robust and accurate background subtraction map. This is a natural fusion because the original resolution and low resolution images have complementary strengths, which original resolution image contains rich information and low resolution image is insensitive to the noises and the small movement of dynamic scene. Experimental result shows that this real-time algorithm is able to detect moving objects accurately even in dynamic scenes.
Feature extraction and classification using Gabor wavelets have proven to be successful in comput... more Feature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.
In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and mult... more In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping method to capture the structure information in faces. Experiments on two face databases, FRGC Version 1 and FRGC Version 2, are conducted to compare the performances of the Gabor and GGabor features, which show that GGabor can also be a powerful tool to model faces, and the Efficient Kernel Fisher classifier can improve the efficiency of the original kernel fisher method.
Feature extraction, discriminant analysis, and classification rule are three crucial issues for f... more Feature extraction, discriminant analysis, and classification rule are three crucial issues for face recognition. This paper presents one method, named GaborfaceSVM, to handle three issues together. For feature extraction, we utilize the Gabor wavelet transform on grey face image to extract Gaborfaces. A Modified Enhanced Fisher Discriminant model is used to reinforce discriminant power of Gaborfaces. During classification process, Support Vector Machines classifier is proposed for robust decision in presence of wide facial and illumination variations. In experiments, the discriminant Gaborfaces approach incorporated with SVMs classifier demonstrates better effectiveness and performance than other methods.
Kernel Fisher Discriminant Analysis (KFDA) has achieved great success in pattern recognition rece... more Kernel Fisher Discriminant Analysis (KFDA) has achieved great success in pattern recognition recently. However, the training process of KFDA is too time consuming (even intractable) for a large training set, because, for a training set with n examples, both its between-class and within-class scatter matrices are of n n × and the time complexity of the KFDA training process is of 3 ( ) O n . Aiming at this problem, this paper employs Bagging technique to decrease the time-space cost of KFDA training process. In addition, this paper is more than just a simple application of Bagging. We have made an important adaptation which can further guarantee the performance of KFDA. Our experimental results demonstrate that the proposed method can not only greatly reduce the cost of time of the training process, but also achieve higher recognition accuracy than traditional KFDA and the simple application of Bagging.
This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), ... more This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The -order LDP is proposed to encode the ( 1) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP).
In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the hi... more In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the histogram of oriented gradient (HOG) features, a set of multi-scale orientation (MSO) features are proposed as the feature representation. The features are extracted on square image blocks of various sizes (called units), containing coarse and fine features in which coarse ones are the unit orientations and fine ones are the pixel orientation histograms of the unit. A cascade of Adaboost is employed to train classifiers on the coarse features, aiming to high detection speed. A greedy searching algorithm is employed to select fine features, which are input into SVMs to train the fine classifiers, aiming to high detection accuracy. Experiments report that our approach obtains state-of-art results with 12.4 times faster than the SVM+HOG method.
In recent years, sparse representation originating from signal compressed sensing theory has attr... more In recent years, sparse representation originating from signal compressed sensing theory has attracted increasing interest in computer vision research community. However, to our best knowledge, no previous work utilizes L1-norm minimization for human detection. In this paper we develop a novel human detection system based on L1-norm Minimization Learning (LML) method. The method is on the observation that a human object can be represented by a few features from a large feature set (sparse representation). And the sparse representation can be learned from the training samples by exploiting the L1-norm Minimization principle, which can also be called feature selection procedure. This procedure enables the feature representation more concise and more adaptive to object occlusion and deformation. After that a classifier is constructed by linearly weighting features and comparing the result with a calculated threshold. Experiments on two datasets validate the effectiveness and efficiency of the proposed method.
Journal of Visual Communication and Image Representation, 2011
This paper proposes a high-order Texture Pattern Flow (TPF) for complex background modeling and m... more This paper proposes a high-order Texture Pattern Flow (TPF) for complex background modeling and motion detection. The pattern flow is proposed to encode the binary pattern changes among the neighborhoods in the space–time domain. To model the distribution of the TPF pattern flow, the TPF integral histograms are used to extract the discriminative features to represent the input video. The
Uploads
Papers by Baochang Zhang