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Gabor Wavelet Selection and SVM Classification for Object Recognition

SHEN Lin-Lin JI Zhen School of Computer,Software Engineering,Shenzhen University,Shenzhen,PRChina,

自动化学报 , 2009,
Abstract: This paper proposes a Gabor wavelets and support vector machine (SVM)-based framework for object recognition. When discriminative features are extracted at optimized locations using selected Gabor wavelets, classifications are done via SVM. Compared to conventional Gabor feature based object recognition system, the system developed in this paper is both robust and efficient. The proposed framework has been successfully applied to two object recognition applications, i.e., object/non-object classification and face recognition. Experimental results clearly show advantages of the proposed method over other approaches.
Face Recognition Based on Two-Dimensional Gabor Wavelets

Cao Lin,Wang Dong-feng,Liu Xiao-jun,Zou Mou-yan,
曹 林

电子与信息学报 , 2006,
Abstract: A new approach based on two-dimensional Gabor wavelets transform for face recognition is presented. The Gabor wavelet representation of an image is the convolution of the image with a family of Gabor kernels. A set of vectors called nodes, over a dense grid of image points are formed, and each node is labeled with a set of complex Gabor wavelets coefficients. The magnitudes of the coefficients are used for recognition. Principal component analysis is a decorrelation technique and its primary goal is to project the high dimensional vectors into a lower dimensional space. Feature nodes, as observation vectors of HMM, is derived by using principal component analysis. A set of images representing different instances of the same person is used to train each HMM, and each individual in the database is represented by an optimal HMM face model. Experimental results show that the proposed algorithm has a high recognition rate with relatively low complexity.
Vehicle category recognition based on Log-Gabor wavelets transform and DS theory

LI Min,HUANG Xi-yue,SHEN Zhi-xi,LI Xiao-wei,

计算机应用研究 , 2009,
Abstract: To solve the problem of vehicle category recognition,this paper proposed a recognition algorithm based on Log-Gabor wavelets transform and DS theory.Used multi-scales Log-Gabor filters to transform the images of vehicles to Log-Gabor vectors and used,SVM of "1-against-1" approach to assign the basic probability numbers.Then,concluded a decision of vehicle category by model of DS theory.Experiment results prove that the introduced algorithm is available.
Facial Expression Representation and Recognition Using 2DHLDA, Gabor Wavelets, and Ensemble Learning  [PDF]
Mahmoud Khademi,Mohammad H. Kiapour,Mehran Safayani,Mohammad T. Manzuri,M. Shojaei
Computer Science , 2010,
Abstract: In this paper, a novel method for representation and recognition of the facial expressions in two-dimensional image sequences is presented. We apply a variation of two-dimensional heteroscedastic linear discriminant analysis (2DHLDA) algorithm, as an efficient dimensionality reduction technique, to Gabor representation of the input sequence. 2DHLDA is an extension of the two-dimensional linear discriminant analysis (2DLDA) approach and it removes the equal within-class covariance. By applying 2DHLDA in two directions, we eliminate the correlations between both image columns and image rows. Then, we perform a one-dimensional LDA on the new features. This combined method can alleviate the small sample size problem and instability encountered by HLDA. Also, employing both geometric and appearance features and using an ensemble learning scheme based on data fusion, we create a classifier which can efficiently classify the facial expressions. The proposed method is robust to illumination changes and it can properly represent temporal information as well as subtle changes in facial muscles. We provide experiments on Cohn-Kanade database that show the superiority of the proposed method. KEYWORDS: two-dimensional heteroscedastic linear discriminant analysis (2DHLDA), subspace learning, facial expression analysis, Gabor wavelets, ensemble learning.
Features Fusion Based on FLD for Face Recognition  [cached]
Changjun Zhou,Qiang Zhang,Xiaopeng Wei,Ziqi Wei
Journal of Multimedia , 2010, DOI: 10.4304/jmm.5.1.63-70
Abstract: In this paper, we introduced a features fusion method for face recognition based on Fisher’s Linear Discriminant (FLD). The method extract features by employed Two-Dimensional principal component analysis (2DPCA) and Gabor wavelets, and then fuse their features which are extracted with FLD respectively. As a holistic feature extraction method, 2DPCA performs dimensional reduction to the input dataset while retaining characteristics of the dataset that contribute most to its variance by eliminating the later principal components. On the contrary, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, which produce salient local features which are most suitable for face recognition. So, we use Gabor wavelets for the local features and then integrate Gabor features with 2DPCA features. In addition to, because FLD could make not only the scatter between classes as large as possible, but the scatter within class as small as possible, the features which are extracted by FLD are reliable for classification. And then, the FLD features of the Gabor and 2DPCA features leads to the application of the support vector machine (SVM) for classification. Finally, the computer simulation illustrates the effectivity of this method on three independent face databases.
Face recognition based on Gabor wavelet and support vector machine

LUO Liang,JIN Wen-biao,GONG Xun,

重庆邮电大学学报(自然科学版) , 2008,
Abstract: 提出一种将Gabor小波和支持向量机相结合的人脸识别算法。运用AdaBoost算法在复杂背景图像中快速准确地检测出人脸部分,进而用Gabor小波提取归一化人脸图像的特征。最后采用支持向量机进行人脸的分类识别。在ORL人脸库和CAS-PEAL-R1人脸库中对算法进行了测试,结果表明该算法识别率较高。
Gabor wavelets combined with volumetric fractal dimension applied to texture analysis  [PDF]
álvaro Gomez Z.,Jo?o B. Florindo,Odemir M. Bruno
Computer Science , 2014, DOI: 10.1016/j.patrec.2013.09.023
Abstract: Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.
HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection  [PDF]
Bo Han,Bo He,Tingting Sun,Mengmeng Ma,Amaury Lendasse
Computer Science , 2014,
Abstract: In this paper, we propose a novel method for fast face recognition called L1/2 Regularized Sparse Representation using Hierarchical Feature Selection (HSR). By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in. It consists of Gabor wavelets and Extreme Learning Machine Auto-Encoder (ELM-AE) hierarchically. For Gabor wavelets part, local features can be extracted at multiple scales and orientations to form Gabor-feature based image, which in turn improves the recognition rate. Besides, in the presence of occluded face image, the scale of Gabor-feature based global dictionary can be compressed accordingly because redundancies exist in Gabor-feature based occlusion dictionary. For ELM-AE part, the dimension of Gabor-feature based global dictionary can be compressed because high-dimensional face images can be rapidly represented by low-dimensional feature. By introducing L1/2 regularization, our approach can produce sparser and more robust representation compared to regularized Sparse Representation based Classification (SRC), which also contributes to the decrease of the computational cost in sparse representation. In comparison with related work such as SRC and Gabor-feature based SRC (GSRC), experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost. Moreover, we also achieve approximate or even better recognition rate.
On new families of wavelets and Gabor analysis  [PDF]
Eyal M. Subag,Ehud Moshe Baruch,Joseph L. Birman,Ady Mann
Mathematics , 2014,
Abstract: We construct two new families of wavelets: One family of frames which is well suited for frequency localized signals and interpolates between the standard wavelet frames and a version of a Gabor type frame. The second family is well suited for time localized signals and interpolated between a version of a wavelet frame and a standard Gabor frame. In particular we approximate Gabor analysis by wavelets. Our construction is based on certain realizations of the unitary representations of the Heisenberg group and of the affine group on L^2(R). The main technical tool that we use for the interpolation procedures is contraction of Lie groups representations.
Personal Identification using Ear Recognition  [cached]
Anam Tariq,M. Usman Akram
TELKOMNIKA : Indonesian Journal of Electrical Engineering , 2012, DOI: 10.11591/telkomnika.v10i2.685
Abstract: The use of biometrics for personal identification or authentication is very common now days. The technology of human ear recognition system is a latest technology in this field. In case of ear biometrics, the shape and appearance remains same throughout the life time of an individual contrary to facial recognition in which change of appearance with expression and age is a major problem. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi ear image database. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively.
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