Local Statistical Features of Dual Tree Complex Wavelet Transform on Parallelogram Image Structure for Face Recognition with Single Sample

Local fusion of complex dual-tree wavelet coefficients based face recognition for single sample problem

Face recognition is the process of identifying a person by comparing his facial image with the existing image in a trained database. The crucial step in face recognition system is the extraction of facial feature. We propose an efficient facial feature representation by using Dual Tree Complex Wavelet Transform (DT-CWT). The Complex WT face characterizes the geometrical structure of facial images by using the properties of DT-CWT such as approximate shift in-variance and good directional selectivity. Since the efficiency retained with DT-CWT is inadequate, a new block design using Dual Tree Complex Wavelet Transform along with efficient normalization and noise reduction techniques is developed and using that design a face recognition system is developed.KeywordsFace RecognitionDiscrete Cosine TransformRecognition RateGabor WaveletFace Recognition SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper introduces a novel DT-CWT feature-based Two-dimensional Inverse FDA (2DIFDA) by integrating the Dual-Tree Complex Wavelet Transform (DT-CWT) of face images and 2DIFDA method for face recognition. The DT-CWT has approximate shift invariance, good directional selectivity and can provide effective feature representation for face images. In the proposed method, DT-CWT is first used to extract the face image features at different scales and orientations. 2DIFDA is then applied for feature selection and dimensionality reduction in the DT-CWT feature space. Experimental results on ORL and FERET face databases demonstrate the feasibility of the new method.

Discrete Wavelet Transform (DWT) has demonstrated advantages in image compression due to its time-frequency resolution property. Dual Tree Complex Wavelet Transform (DTCWT) in addition to the advantages of DWT supports additional properties such as directionality and shift invariant. DTCWT has coefficient redundancy of 4:1 or 2 m :1 in m dimensions thus is four time complex than DWT computation. In this work, DTCWT based image compression algorithm is proposed that performs decomposition using DTCWT and reconstruction is carried out using DWT. The DTCWT bands are companded and encoded using entropy encoder and the quantization of the sub bands introduces minimum loss. The proposed algorithm achieves 77% compression with 40 dB PSNR thus suitable for real time image compression. DTCWT filters are appropriately selected to achieve perfect reconstruction.

Biometric technology is considered as more effective technology for identification and recognition of individuals in most secure environments. Face recognition is a technique based on physiological biometric characteristics. In this paper, we propose Face Recognition using DT-CWT and LBP Features (FRDL) for different databases. The original face image is resized to uniform dimensions of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sup> . The five levels Dual-Tree Complex Wavelet Transform (DT-CWT) is applied on face image to obtain DT-CWT coefficients. The matrix of DT-CWT coefficients is segmented in to 3×3 matrixes. The Local Binary Pattern (LBP) algorithm is applied on each 3×3 matrix to get final features. The Euclidean Distance (ED) is used to compare features of test face image with data base images. It is observed that the values of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are better in the proposed model compare to existing method.

Dual-tree complex wavelet transform (DTCWT) is of better properties than discrete wavelet transform (DWT). The attention of the researches was aroused by the noise and its influence on the performance of algorithm in genetic operation of floating point representation genetic algorithm (FPRGA). In order to eliminate the noise influence on algorithm properties, this paper proposes denoising mutation on floating point representation (FPR) with DTCWT in genetic operation. It presents a FPR denoising mutation genetic algorithm based on DTCWT (FDMGAD). It makes some experiments. The result of the research and the experiments indicates which FDMGAD is superior to other algorithms. The method is reliable in theory, is feasible in technique.

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

The purpose of this study is to compare image denoising techniques based on real and complex wavelet-transforms. Possibilities provided by the classical discrete wavelet transform (DWT) with hard and soft thresholding are considered, and influences of the wavelet basis and image resizing are discussed. The quality of image denoising for the standard 2-D DWT and the dual-tree complex wavelet transform (DT-CWT) is studied. It is shown that DT-CWT outperforms 2-D DWT at the appropriate selection of the threshold level.

In this paper, a fully automatic framework is proposed for 3D face recognition and its superiority performance is justified by the FRGC v2 data. For 3D data preprocessing, a new face smoothing meth...

Automatic ECG arrhythmia classification using dual tree complex wavelet based features

In this paper, a novel image denosing scheme is proposed by applying 2D dual-tree complex wavelet transform (DTCWT) to the second bandelet transform. Compared with traditional discrete wavelet transform (DWT), the DTCWT has nearly shift invariant and directionally selective in two and higher dimensions important properties, which are suitable for image denoising. The bandelet transform has offer an asymptotically optimal representation for geometric images, but it lacks translation invariant. Firstly, the DTCWT is employed to obtain subands, and then bandeletization is implemented in each subband. During finding the best geometrical flow and optimal quadtree segment, the cost term of Lagrangian is confirmed by Bayes estimator. At last, Bayes soft-threshold shrinkage denoising in bandelet transform domain is implemented. The experiments show that the proposed denoising method has better results than several DTCWT-based methods for image with rich directional features. The denoised images recovered by the proposed scheme are visually more appealing.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

Iris is a rich-textured image having randomly oriented singularities (edges) in all directions and frequencies. Hence, Gabor wavelet-based representation of iris images is an excellent solution. However, non-orthogonal band-pass filters tuned to one particular frequency and one particular orientation based on representation are computationally inefficient. On the other hand, discrete wavelet transform (DWT) is orthogonal and computationally efficient but provides edges only in three orientations which are not sufficient to preserve a uniqueness of an iris. Therefore, to overcome this limitation of DWT and exploits its strength of computational efficiency, the use of dual-tree complex wavelet transform (DT-CWT) and rotated complex wavelet filters (RCWF) has been used for feature extraction. The detail analysis of Gabor filters and DWT has been presented in this chapter. Theoretical aspects of wavelet transform and design of DWT filters, DT-CWT and RCWF, has been described in this chapter. Implementation issues and analysis of iris with adequate experimentations and deliberate comparison with existing algorithms are the key points of this chapter.

This paper introduces an efficient technique for lung abnormalities segmentation in CT images based on the use of dual-tree complex wavelet transform (DT-CWT) and multilevel histogram thresholding. Recently, a scalar wavelet-based method has shown favorable results compared with previous approaches in honeycomb detection in pediatric CT images. Using our recently designed dual-tree complex filter bank and employing high resolution intensity similarities, we show that DT-CWT outperforms the results obtained with discrete wavelet transform (DWT) in general. Our early experiments show that multi-wavelets (MW) can also present a promising performance than DWT. The results indicate that DT-CWT performs slightly better than multi-wavelets, however, it can significantly outperform scalar wavelets. The former is probably due to better edge preserving property of multi-wavelets, while the latter is obtained because of good directionality and shift-invariance of dual-tree complex wavelet transform.

Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. Then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. The experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.