Language English View all editions Prev Next edition 2 of 2. Author Chan, Andrew K. Other Authors Liu, Steve J.
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Physical Description vi,73p. Subjects Signal processing -- Mathematics. Image processing -- Mathematics. Wavelets Mathematics Notes In box. Bibliography: p View online Borrow Buy Freely available Show 0 more links None of your libraries hold this item.
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The convergence area of our proposed method almost quadruples compared to ASM. The Haar-wavelet transform successfully compensates for additional cost of using 2-D texture features. The algorithm has also been tested in practice with a webcam, giving near real-time performance and good extraction results. The extraction of required features from the facial image is an important primitive task for face recognition. The paper  evaluates different nonlinear feature extraction approaches, namely wavelet transform, radon transform and cellular neural networks CNN.
The scalability of the linear subspace techniques is limited as the computational load and memory requirements increase dramatically with the large database. In this work, the combination of radon and wavelet transform based approach is used to extract the multi- resolution features, which are invariant to facial expression and illumination conditions. The efficiency of the stated wavelet and radon based nonlinear approaches over the databases is demonstrated with the simulation results performed over the FERET database.
This paper also presents the use of CNN in extracting the nonlinear facial features. The detailed description of the proposed methodology is given in the next section. The methodology is shown in Fig 1. The method involves two phase namely training phase and testing phase. The detailed description of each phase is given in the following sub sections. The classification makes use of features extracted using discrete wavelet transform approach form face image samples.
The original images are converted into gray scale images. For each block Discrete Wavelet Transform is applied. The neural network architecture that is most commonly used with the back propagation algorithm is the multilayer feed-forward network. In training phase the artificial neural network is trained using Back Propagation feed forward neural model.
These two pair of files is then given to the neural network which then trains itself accordingly.
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The training takes place such that the neural network learns that the neural network learns that each entry in the input file has a corresponding entry in the output file. Proposed Block Diagram for Recognition of face 3. Database consists of frontal face images and with same background. The sample images are shown in Fig 2.
Sample Images 3.
One or more descriptors of an object or an entity an image from the pattern. In other words, a pattern is an arrangement of descriptors. The descriptors also called features in pattern recognition literature. The features are necessary for differentiating one class of objects from another. A method must be used for describing the objects so that features of interest are highlighted.
Step 4 : These co-efficient are stored in an array. The original image is converted into gray scale image. For each block, Discrete Wavelet Transform is applied. It computes approximation coefficients matrix and details coefficients matrices horizontal, vertical, and diagonal, respectively , of each block of the image. The next page shows the 12 blocks with first level decomposition.
Classification Model The features are stored for each 15 image with different expressions of 20 different persons. The classification is carried out using only one type of feature set that consists of all 24 features ie 2 features from each 12 blocks of the image. The output layer consists of 20 nodes represented in binary digits. The output is given in Table 1 for recognizing face. Table 1.
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Output Pattern for Recognition Person Output Pattern Person Output Pattern Person 1 Person 11 Person 2 Person 12 Person 3 Person 13 Person 4 Person 14 Person 5 Person 15 Person 6 Person 16 Person 7 Person 17 Person 8 Person 18 Person 9 Person 19 Person 10 Person 20 4. The original image is converted into gray scale image as shown in Fig 3. The Fig 4 shows the 12 blocks with first level decomposition. TABLE 2.
An Experimental Analysis dealing with various issues Our database consists of total images out of those images have been used to train the neural network and images have been used for testing against the trained images, and the following analysis have been obtained. From trained images images are correctly matched only 3 images match is not found. From testing images 90 images have been perfectly matched. Out for total of 13 where mismatched, we have obtained an accuracy of The overall performance of the system after conducting the experimentation on the dataset is reported in Table 3.
TABLE 3. In this project we have designed one of the best approaches to recognize the faces. This method uses wavelet transform for extracting feature vectors. The following alternative approach can be used. For initialization, the user may use either first- or second-order B-splines. The iteration should converge after 5 to 10 iterations if the regularity of the function is high.
However, for some sequences designed by the perfect reconstruction filter bank approach, the algorithm may not even converge at all. In other words, there are filter banks that are not associated with scaling functions and wavelets. The final data may be seen by using the usual 1-D graphic display. The code to generate the scaling function in this Toolware is based on this algorithm.
We outline the procedure as follows: 1. Initialize the program by setting all data files to zero. Set desired number of iteration, say 10, and set the iteration index to 1. Input the initial trial function 0 0 X. One may use an impulse function, a rectangular pulse i. Carry out 3.
Wavelet Toolware_software for Wavelet Training | Wavelet | Spectral Density
Upsample the resulting sequence by inserting zeros in between every other data point. This sequence is 01 x. Increase the iteration index by 1 and repeat steps 4 to 6 until 10 iteration cycles have been completed. For the spectral-domain approach, we simply multiply the infinite product in 2.
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We use the time-domain approach in this Toolware to generate the graphs of different wavelets. The reader should use the Toolware to view different wavelets as given in Part C. For processing of large images e. We compute the dual coefficients ck,j by Since B-splines have compact support, the autocorrelation sequence is a finite sequence where so that can be computed very efficiently. If the final objective of the processing requires the reconstruction of the signal, as in image compression, one needs to eventually convert the processed coefficients back to B-spline coefficients for image quality evaluation and display.
However, if the processing objective is for detection and recognition, the dual-spline coefficients can be used for neural network training purposes. In terms of digital signal processing symbolism, it is given in Fig 1. The computation of the wavelet coefficients at one level of resolution is carried out in a similar manner, namely, Comine these two steps to form the decomposition block as shown later. Implementation of 2. These procedures are repeated to yield the coefficients at lower resolution levels.
roffflamcalsingsil.ml The scaling function coefficients at a higher resolution level are computed by using the formula Each summation can be interpreted as a convolution process after upsampling. This process is depicted in Fig 1. The reader should use the Toolware to practice this algorithm for 1-D signals. It is used in noise reduction, in signal and image compression, and sometimes in signal recognition. The four types of thresholding we use are l hard thresholding, 2 soft thresholding, 3 quantile thresholding, and 4 universal thresholding.
The choice of thresholding methods depends on the application. We discuss each type here briefly. If a signal or a coefficient value is below a preset value, it is set to zero.