Journal articles: 'PCA AND DWT'

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Takale, Swapnil, and Dr Altaaf Mulani. "DWT-PCA based Video Watermarking." Journal of Electronics,Computer Networking and Applied Mathematics, no. 26 (November 17, 2022): 1–7. http://dx.doi.org/10.55529/jecnam.26.1.7.

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Progressed watermarking video may be a methodology for embedding additional data another to video salute. Embedded data is utilized for proprietor copyright or recognizable affirmation. It added up to approach for watermarking is shown in this System, by utilizing Discrete Wavelet Alter (DWT) and Crucial Component Examination (PCA). There are a number of watermarking strategies like DCT, DWT, and DWT-SVD, but there's a downside inside the watermarking to stand up to attacks. In this way the cutting edge progressed picture watermarking calculation is proposed which provide solid watermarking with insignificant whole of bending in case of ambushes. DWT offers flexibility and PCA makes a distinction in diminishing relationship among the wavelet coefficients.

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HIEN, THAI DUY, YEN-WEI CHEN, and ZENSHO NAKAO. "ROBUST DIGITAL WATERMARKING BASED ON PRINCIPAL COMPONENT ANALYSIS." International Journal of Computational Intelligence and Applications 04, no. 02 (June 2004): 183–92. http://dx.doi.org/10.1142/s1469026804001240.

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We propose a robust digital watermarking technique based on Principal Component Analysis (PCA) and evaluate the effectiveness of the method against some watermark attacks. In this proposed method, watermarks are embedded in the PCA domain and the method is closely related to DCT or DWT based frequency-domain watermarking. The orthogonal basis functions, however, are determined by data and they are adaptive to the data. The presented technique has been successfully evaluated and compared with DCT and DWT based watermarking methods. Experimental results show robust performance of the PCA based method against most prominent attacks.

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J, Madhavan. "Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image Retrieval." Computer Science & Engineering: An International Journal 2, no. 6 (December 31, 2012): 41–50. http://dx.doi.org/10.5121/cseij.2012.2604.

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Upadhyaya, Prashant, Omar Farooq, M. R. Abidi, and Priyanka Varshney. "Comparative Study of Visual Feature for Bimodal Hindi Speech Recognition." Archives of Acoustics 40, no. 4 (December 1, 2015): 609–19. http://dx.doi.org/10.1515/aoa-2015-0061.

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Abstract In building speech recognition based applications, robustness to different noisy background condition is an important challenge. In this paper bimodal approach is proposed to improve the robustness of Hindi speech recognition system. Also an importance of different types of visual features is studied for audio visual automatic speech recognition (AVASR) system under diverse noisy audio conditions. Four sets of visual feature based on Two-Dimensional Discrete Cosine Transform feature (2D-DCT), Principal Component Analysis (PCA), Two-Dimensional Discrete Wavelet Transform followed by DCT (2D-DWT- DCT) and Two-Dimensional Discrete Wavelet Transform followed by PCA (2D-DWT-PCA) are reported. The audio features are extracted using Mel Frequency Cepstral coefficients (MFCC) followed by static and dynamic feature. Overall, 48 features, i.e. 39 audio features and 9 visual features are used for measuring the performance of the AVASR system. Also, the performance of the AVASR using noisy speech signal generated by using NOISEX database is evaluated for different Signal to Noise ratio (SNR: 30 dB to −10 dB) using Aligarh Muslim University Audio Visual (AMUAV) Hindi corpus. AMUAV corpus is Hindi continuous speech high quality audio visual databases of Hindi sentences spoken by different subjects.

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A. Patil, Supriya. "Digital Video Watermarking Using Dwt And Pca." IOSR Journal of Engineering 3, no. 11 (November 2013): 45–49. http://dx.doi.org/10.9790/3021-031144549.

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Kabir Rana, Humayan, Md Shafiul Azam, and Mst Rashida Akhtar. "Iris Recognition System Using PCA Based on DWT." SM Journal of Biometrics & Biostatistics 2, no. 3 (2017): 1–5. http://dx.doi.org/10.36876/smjbb.1015.

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Deepa, S., and V. Vijaya Chamundeeswari. "Genetic Based Face Recognition for Healthcare Applications." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 593–603. http://dx.doi.org/10.1166/jmihi.2020.2965.

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Face recognition is a significant biometric credential in the field of security authentication. It additionally assumes a noteworthy job in image processing and it is applicable in various systems like verifying the identity of the person and in security purpose. Recognizing the face with varying background, poses and illumination are the complexity involved in this face recognition. Many algorithms exist for face recognition, of which, Discrete Wavelet Transform (DWT) with Principal Component Analysis (PCA) works better for recognition of faces. An algorithm using 3 Level-DWT and modified PCA is proposed for feature extraction. The PCA and reconstruction of images using Inverse PCA, help not only for dimensionality reduction, but also to find the least principal components (PC) of an image from which the significant features of a face image can be extracted. The significant features thus extracted are used for classifying genetic and non-genetic faces. Using extracted features from 3 level DWT and PCA, Support vector machine (SVM) is utilized to classify the faces genetically. The proposed extracted features does not intend to certain features like ears, nose and eyes of the face, but corresponds to identify the faces which are genetically similar. Based on the statistical measure analysis, the proposed algorithm 3 Level dwt with modified PCA works well in extracting the features for identifying the faces which are genetically closer. This face recognition application system can be effectively used to treat a patient in other location with complete security. There is no chance for data stealing, since the concerned doctors and patient only will take part in the system. The identification of genetic faces will turn out to be an achievement in the field of health care monitoring systems.

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Rana, Humayan Kabir, Md Shafiul Azam, Mst Rashida Akhtar, Julian M. W. Quinn, and Mohammad Ali Moni. "A fast iris recognition system through optimum feature extraction." PeerJ Computer Science 5 (April 8, 2019): e184. http://dx.doi.org/10.7717/peerj-cs.184.

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With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

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Michael Zimba, and Sun Xingming. "DWT-PCA (EVD) Based Copy-move Image Forgery Detection." International Journal of Digital Content Technology and its Applications 5, no. 1 (January 31, 2011): 251–58. http://dx.doi.org/10.4156/jdcta.vol5.issue1.27.

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Ghadekar, Premanand Pralhad, and Nilkanth Bhikaji Chopade. "Modelling Nonlinear Dynamic Textures using Hybrid DWT–DCT and Kernel PCA with GPU." Journal of The Institution of Engineers (India): Series B 97, no. 4 (June 21, 2016): 549–55. http://dx.doi.org/10.1007/s40031-016-0220-1.

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Asiedu, Louis, Bernard O. Essah, Samuel Iddi, K. Doku-Amponsah, and Felix O. Mettle. "Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images." Journal of Applied Mathematics 2021 (April 7, 2021): 1–8. http://dx.doi.org/10.1155/2021/5541522.

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The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.

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Damayanti, Merry Annisa, Suhardjo Sitam, Bambang Hidayat, and Ivhatry Rizky Octavia Putri Susilo. "Image processing of periapical radiograph on granuloma detection by analysis method based on Android." Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI) 5, no. 1 (April 30, 2021): 1. http://dx.doi.org/10.32793/jrdi.v5i1.672.

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Objectives: The study assesses periapical radiograph image with various android based analysis method to detect granuloma. Materials and Methods: The study uses survey descriptive cross sectional by using questionnaire. The questionnaire is distributed to 70 random respondents. The methods of the android application used are BLOB (Binary Large Object), DCT and LDA (Discrete Cosine Transform and Linier Discriminant Analysis), DWT and PCA (Discrete Wavelet Transform & Principal Component Analysis), and multiwavelet transformation. The questionnaire assessment included accuracy, effectiveness, attractiveness, innovativeness of the android application. Results: Android application with BLOB has effectivity and accuracy of 62,5%, attractiveness and innovativeness of 75%. Android application with DCT and LDA has effectivity and accuracy of 50 %, attractiveness of 70% and innovativeness of 80%. Android application with DWT and PCA has effectivity of 50%, accuracy of 60%, attractiveness of 66,66% and innovativeness of 80%. Android application with multiwavelet transformation has effectivity and accuracy of 50%, attractiveness of 55% and innovativeness of 73%. Conclusion: Based on assessment, the four methods used to detect granuloma are effective and applicative with android-based application. Android-based Application can detect granuloma with approximately more than 70% successful rate. These methods ease the practitioner to interpret the granuloma image.

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Charfi Marrakchi, O., C. Masmoudi Charfi, M. Hamzaoui, and H. Habaieb. "Improvement of Sentinel-1 Remote Sensing Data Classification by DWT and PCA." Journal of Sensors 2021 (February 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/8897303.

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This article presents a new alternative for data resource, by applying the proposed methods of Principal Components Analysis (PCA) or of Discrete Wavelet Transformation (DWT) on the VV and VH polarization images of the Sentinel-1 radar satellite, aiming at a better classification of data. The study area concerns the Houareb site located in the city of Kairouan in central Tunisia. In addition to Sentinel-1 data, field truth data and the Euclidian Minimum Distance (EMD) criterion were used for classification and validation. Energy descriptors have been proposed in this study for classifications. Cross validation was used to evaluate the results of the classification. The best classification result was achieved using the DWT method applied on VH and VV images with an Overall Precision (OA) of 0.671 and 0.548, respectively, against an OA value of 0.371 and of 0.449 when the PCA method and the Minimum Distance (MDist) classifier were applied on the dual (VV; VH) polarization, respectively. The DWT transformation gives the highest Kappa Precision Coefficient (KPC) of 0.8.

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Cao, Zhen, Yongying Liu, and Jiancheng Zhao. "Efficient Discrimination of Some Moss Species by Fourier Transform Infrared Spectroscopy and Chemometrics." Journal of Spectroscopy 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/191796.

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Fourier transform infrared spectroscopy (FTIR) technique was used to classify 16 species from three moss families (Mielichhoferiaceae, Bryaceae, and Mniaceae). The FTIR spectra ranging from 4000 cm−1to 400 cm−1of the 16 species were obtained. To group the spectra according to their spectral similarity in a dendrogram, cluster analysis and principal component analysis (PCA) were performed. Cluster analysis combined with PCA was used to give a rough result of classification among the moss samples. However, some species belonging to the same genus exhibited very similar chemical components and similar FTIR spectra. Fourier self-deconvolution (FSD) was used to enhance the differences of the spectra. Discrete wavelet transform (DWT) was used to decompose the FTIR spectra ofMnium laevinerveandM. spinosum. Three scales were selected as the feature extracting space in the DWT domain. Results showed that FTIR spectroscopy combined with DWT was suitable for distinguishing different species of the same genus.

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Alfarhan, Khudhur A., Mohd Yusoff Mashor, Abdul Rahman Mohd Saad, Hayder A. Azeez, and Mustafa M. Sabry. "Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification." Journal of Biomimetics, Biomaterials and Biomedical Engineering 30 (January 2017): 1–11. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.30.1.

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Arrhythmia, a common form of heart disease, can be detected from an electrocardiogram (ECG) signal. This research work presents a comparative study between five feature extraction methods applied separately on two window sizes for detecting three ECG pulse types, namely normal and two arrhythmia variations. The library support vector machine (LIBSVM) was used to classify the three classes of the ECG pulses. The ECG signals were obtained from MIT-BIH database. The ECG dataset was normalized and filtered to remove any noise and after that the signals were windowed into two window sizes (long window and short window). Five approaches were used to extract the features from the ECG signals. These approaches are scalar Autoregressive model coefficients, Haar discrete wavelet transform (DWT), Daubechies (db) DWT, Biorthogonal (bior) DWT, and principal components analysis (PCA). Each approach was applied separately on the two window sizes. The results of the classification show that scalar Autoregressive model coefficients, Haar, db, and bior are better approaches to catch the ECG features for short window than the long window. However, PCA gave the closest and highest results for the two window sizes than other approaches. That mean the PCA is the better feature extraction approach for both window sizes.

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Shivahare, Basu Dev, and S. K. Gupta. "Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm." Journal of Healthcare Engineering 2022 (March 30, 2022): 1–19. http://dx.doi.org/10.1155/2022/9009406.

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This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person’s life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu’s maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.

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FERNÁNDEZ-MARTÍNEZ, JUAN LUIS, and ANA CERNEA. "NUMERICAL ANALYSIS AND COMPARISON OF SPECTRAL DECOMPOSITION METHODS IN BIOMETRIC APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 01 (February 2014): 1456001. http://dx.doi.org/10.1142/s0218001414560011.

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Face recognition is a challenging problem in computer vision and artificial intelligence. One of the main challenges consists in establishing a low-dimensional feature representation of the images having enough discriminatory power to perform high accuracy classification. Different methods of supervised and unsupervised classification can be found in the literature, but few numerical comparisons among them have been performed on the same computing platform. In this paper, we perform this kind of comparison, revisiting the main spectral decomposition methods for face recognition. We also introduce for the first time, the use of the noncentered PCA and the 2D discrete Chebyshev transform for biometric applications. Faces are represented by their spectral features, that is, their projections onto the different spectral basis. Classification is performed using different norms and/or the cosine defined by the Euclidean scalar product in the space of spectral attributes. Although this constitutes a simple algorithm of unsupervised classification, several important conclusions arise from this analysis: (1) All the spectral methods provide approximately the same accuracy when they are used with the same energy cutoff. This is an important conclusion since many publications try to promote one specific spectral method with respect to other methods. Nevertheless, there exist small variations on the highest median accuracy rates: PCA, 2DPCA and DWT perform better in this case. Also all the covariance-free spectral decomposition techniques based on single images (DCT, DST, DCHT, DWT, DWHT, DHT) are very interesting since they provide high accuracies and are not computationally expensive compared to covariance-based techniques. (2) The use of local spectral features generally provide higher accuracies than global features for the spectral methods which use the whole training database (PCA, NPCA, 2DPCA, Fisher's LDA, ICA). For the methods based on orthogonal transformations of single images, global features calculated using the whole size of the images appear to perform better. (3) The distance criterion generally provides a higher accuracy than the cosine criterion. The use of other p-norms (p > 2) provides similar results to the Euclidean norm, nevertheless some methods perform better. (4) No spectral method can provide 100% accuracy by itself. Therefore, other kind of attributes and supervised learning algorithms are needed. These results are coherent for the ORL and FERET databases. Finally, although this comparison has been performed for the face recognition problem, it could be generalized to other biometric authentication problems.

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Mensah, Joseph Agyapong, Louis Asiedu, Felix O. Mettle, and Samuel Iddi. "Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints." Journal of Applied Mathematics 2021 (September 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/7060270.

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Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.

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Alalayah, Khaled M., Ebrahim Mohammed Senan, Hany F. Atlam, Ibrahim Abdulrab Ahmed, and Hamzeh Salameh Ahmad Shatnawi. "Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means." Diagnostics 13, no. 11 (June 3, 2023): 1957. http://dx.doi.org/10.3390/diagnostics13111957.

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Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

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Sujatha, P., and R. Devi. "Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT." International Journal of Intelligent Enterprise 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijie.2021.10035656.

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N. Farhan, Rabah, Salah A. Aliesawi, and Zahraa Z. Abdulkareem. "PCA and DWT with Resilient ANN based Organic Compounds Charts Recognition." International Journal of Computer Applications 88, no. 1 (February 14, 2014): 22–27. http://dx.doi.org/10.5120/15316-3615.

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Saurabh, Phadtare. "Video Watermarking Scheme Based on DWT and PCA for Copyright Protection." IOSR Journal of Computer Engineering 9, no. 4 (2013): 18–24. http://dx.doi.org/10.9790/0661-0941824.

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Devi, R., and P. Sujatha. "Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT." International Journal of Intelligent Enterprise 10, no. 1 (2023): 73. http://dx.doi.org/10.1504/ijie.2023.127237.

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Hu, Tao, Wen-Ying Jin, and Cun-Gui Cheng. "Classification of Five Kinds of Moss Plants with the Use of Fourier Transform Infrared Spectroscopy and Chemometrics." Spectroscopy 25, no. 6 (2011): 271–85. http://dx.doi.org/10.1155/2011/908150.

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Fourier transform infrared spectroscopy (FT-IR) with Horizontal Attenuated Total Reflectance (HATR) techniques is used to obtain the FT-IR spectra of five kinds of mosses, such asPtychomitrium dentatum(Mitt.) Jaeg.,Ptychomitrium polyphylloides(C. Muell.) Par.,Ptychomitrium sinense(Mitt.) Jaeg.,Macromitrium syntrichophyllumTher. Etp. Vard., andMacromitrium ferrieiCard. Sz Ther. Based on the comparison of the above mosses in the FT-IR spectra, the region ranging from 4000 to 650 cm−1was selected as the characteristic spectra for analysis. Principal component analysis (PCA) and cluster analysis are considered to identify the five moss species. Because they belong to the homogeneous plants, and have similar chemical components and close FT-IR spectroscopy, PCA and cluster analysis can only give a rough result of classification among the five moss species, Fourier self-deconvolution (FSD) and discrete wavelet transform (DWT) methods are used to enhance the differences between them. We use these methods for further study. Results show that it is an excellent method to use FT-IR spectroscopy combined with FSD and DWT to classify the different species in the same family. FT-IR spectroscopy combined with chemometrics, such as FSD and DWT, can be used as an effective tool in systematic research of bryophytes.

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Ahmed, Ghali, and Benyettou Mohamed. "Improving the Recognition of Faces using PCA and SVM Optimized by DWT." International Journal of Computer Applications 107, no. 17 (December 18, 2014): 7–11. http://dx.doi.org/10.5120/18841-0136.

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Asiedu, Louis, Felix O. Mettle, Ezekiel N. N. Nortey, and Enoch S. Yeboah. "RECOGNITION OF FACE IMAGES UNDER ANGULAR CONSTRAINTS USING DWT-PCA/SVD ALGORITHM." Far East Journal of Mathematical Sciences (FJMS) 102, no. 11 (December 12, 2017): 2809–30. http://dx.doi.org/10.17654/ms102112809.

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El abbadi, Nidhal Khdhair, and Zahraa Faisal Shoman. "Detection and recognition of brain tumor based on DWT, PCA and ANN." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 56. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp56-63.

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Brain tumor is one of more dangerous diesis that affected more than 100 persons every day. The challenge is how to detect and recognise benign and malignant tumor without surgery. In this paper, initially, brain images are filtered to remove unwanted particles, then a new method for automatic segmentation of lesion area is carried out based on mean and standard deviation. Combining both solidity property and morphological operation used to detect only the tumor from segmented image. Mathematical morphology such as close used to join narrow breaks regions in an object, fill the small holes and remove small objects. Features extracted from image by using wavelet transform, followed by applying principle component analysis (PCA) to reduce the dimensions of features. Classification of tumor based on neural network, where the inputs to the network are thirteen statistical features and textural features. The algorithm is trained with 20 of brain MRI images and tested with 45 brain MRI images. Accuracy for this method was encourage and reach near 100% in identifying normal and abnormal tissues from MRI images.

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Abba, S. I., Gozen Elkiran, and Vahid Nourani. "Improving novel extreme learning machine using PCA algorithms for multi-parametric modelling of municipal wastewater treatment plant." DESALINATION AND WATER TREATMENT 215 (2021): 414–26. http://dx.doi.org/10.5004/dwt.2021.26903.

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Liu, Liang, Ting-ting Cao, Xiao-dong Wang, Zhou Dandan, and Chong-wei Cui. "Spatio-temporal variability and water quality assessment of the Mudan River Watershed, Northern China: PCA and WQI." DESALINATION AND WATER TREATMENT 238 (2021): 38–48. http://dx.doi.org/10.5004/dwt.2021.27758.

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Wu, Jiang-Long, and Xiao-Lin Tian. "Image Fusion for Mars Data Using Mix of Robust PCA." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 01 (January 2017): 1754002. http://dx.doi.org/10.1142/s0218001417540027.

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Multi-sensor image fusion is the process of combining relevant information from high spatial resolution image and high spectral resolution image. This paper proposes a pansharpening method for the fusion of Mars images obtained by the Mars Reconnaissance Orbiter satellite (PAN) and Mars Odyssey satellite (THEMIS MS). The method is based on some mix of Intensity, Hue, Saturation (IHS) and robust principal component analysis (RPCA) combined with the discrete wavelet transformation (DWT). Meanwhile, the results obtained with that of the other fusion techniques are compared, and a relatively objective comprehensive evaluation for fused image is used. Experiments show that the proposed algorithm has a significant suppression of artificial textures and less spectral distortion, and its running time is acceptable.

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EzEldeen, M., J. Wyatt, A. Al-Rimawi, W. Coucke, E. Shaheen, I. Lambrichts, G. Willems, C. Politis, and R. Jacobs. "Use of CBCT Guidance for Tooth Autotransplantation in Children." Journal of Dental Research 98, no. 4 (February 20, 2019): 406–13. http://dx.doi.org/10.1177/0022034519828701.

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Tooth autotransplantation (TAT) offers a viable biological approach to tooth replacement in children and adolescents. The aim of this study was to evaluate the outcome of the cone-beam computed tomographic (CBCT)–guided TAT compared to the conventional TAT protocol and to assess the 3-dimensional (3D) patterns of healing after CBCT-guided TAT (secondary aim). This study included 100 autotransplanted teeth in 88 patients. Each experimental group consisted of 50 transplants in 44 patients (31 males and 19 females). The mean (SD) age at the time of surgery was 10.7 (1.1) y for the CBCT-guided group. This was 10.6 (1.3) y for the conventional group. The mean (SD) follow-up period was 4.5 (3.1) y (range, 1.1 to 10.4 y). Overall survival rate for the CBCT-guided TAT was 92% with a success rate of 86% compared to an 84% survival rate and a 78% success rate for the conventional group ( P > 0.005). The following measurements were extracted from the 3D analysis: root hard tissue volume (RV), root length (RL), apical foramen area (AFA), and mean and maximum dentin wall thickness (DWT). Overall, the mean (SD) percentage of tissue change was as follows: RV gain by 65.8% (34.6%), RL gain by 37.3% (31.5%), AFA reduction by 91.1% (14.9%), mean DWT increase by 107.9% (67.7%), and maximum DWT increase by 26.5% (40.1%). Principal component analysis (PCA) identified the mean DWT, RV, and maximum DWT as the parameters best describing the tissue change after TAT. Cluster analysis applied to the variables chosen by the PCA classified the CBCT group into 4 distinct clusters (C1 = 37.2%, C2 = 17.1%, C3 = 28.6%, C4 = 17.1%), revealing different patterns of tissue healing after TAT. The CBCT-guided approach increased the predictability of the treatment. The 3D analysis provided insights into the patterns of healing. CBCT-guided TAT could be adopted as an alternative for the conventional approach. (Clinical trial center and ethical board University Hospitals, KU Leuven: S55287; ClinicalTrials.gov Identifier: NCT02464202)

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Guharoy, Rabel, Nanda Dulal Jana, and Suparna Biswas. "An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers." Journal of Physics: Conference Series 2286, no. 1 (July 1, 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2286/1/012013.

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Abstract This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) with Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, Principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the main features in low dimensional feature space. Three classifiers name: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) classifier have been used in the proposed work for classifying the EEG signals. The raised method is tested over Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

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S, Guruprasad, Kurian M Z, Suma H N, and Sharanabasava raj. "A MEDICAL MULTI-MODALITY IMAGE FUSION OF CT/PET WITH PCA, DWT METHODS." ICTACT Journal on Image and Video Processing 4, no. 2 (November 1, 2013): 677–81. http://dx.doi.org/10.21917/ijivp.2013.0098.

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Borra, Surya, Rajesh Panakala, and Pullakura Kumar. "VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule." International Journal of Intelligent Engineering and Systems 12, no. 5 (October 31, 2019): 1–11. http://dx.doi.org/10.22266/ijies2019.1031.01.

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Sabut, Sukanta, Santanu Sahoo, and Monalisa Mohanty. "Automated ECG beat classification using DWT and Hilbert transform-based PCA-SVM classifier." International Journal of Biomedical Engineering and Technology 32, no. 3 (2020): 287. http://dx.doi.org/10.1504/ijbet.2020.10027745.

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Sahoo, Santanu, Monalisa Mohanty, and Sukanta Sabut. "Automated ECG beat classification using DWT and Hilbert transform-based PCA-SVM classifier." International Journal of Biomedical Engineering and Technology 32, no. 3 (2020): 287. http://dx.doi.org/10.1504/ijbet.2020.106037.

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Ahmad Khorsheed, Eman. "Detection of Abnormal electrocardiograms Based on Various Feature Extraction methods." Academic Journal of Nawroz University 12, no. 3 (July 2, 2023): 111–19. http://dx.doi.org/10.25007/ajnu.v12n3a1818.

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Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart obtained by placing various electrodes on specific areas of the subject's body surface. Abnormalities in a patient's ECG signal may indicate cardiac diseases that require immediate medical attention. As a result, detecting an abnormal ECG is critical for the patient's benefit. This work develops a method for classifying ECG signals as normal or abnormal. In this paper, we propose a method for detecting cardiac arrhythmias in electrocardiograms (ECG). In the first stage, the proposal focuses on various feature extractor methods. The Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and Improved complete ensemble empirical mode decomposition with adaptive noise were the feature extraction techniques evaluated (ICEEMDAN). The PCA method is then used to reduce the number of features. Finally, for classification, the Support Vector Machine (SVM) was used, which was trained using the features extracted in the first stage. The proposed models are tested using datasets from MIT-BIH arrhythmia and PTB Diagnostics. The experimental results show that using 3-PCs with the DWT method produces better results than the other methods, which achieve 98.7% in terms of accuracy.

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Lin, Ruijing, Chaoyi Dong, Pengfei Ma, Shuang Ma, Xiaoyan Chen, and Huanzi Liu. "A Fused Multidimensional EEG Classification Method Based on an Extreme Tree Feature Selection." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/7609196.

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When a brain-computer interface (BCI) is designed, high classification accuracy is difficult to obtain for motor imagery (MI) electroencephalogram (EEG) signals in view of their relatively low signal-to-noise ratio. In this paper, a fused multidimensional classification method based on extreme tree feature selection (FMCM-ETFS) is proposed for discerning motor imagery EEG tasks. First, the EEG signal was filtered by a Butterworth filter for preprocessing. Second, C3, C4, and CZ channels were selected to extract time-frequency domain and spatial domain features using autoregressive (AR), common spatial pattern (CSP), and discrete wavelet transform (DWT). The extracted features were fused for a further feature elimination. Then, the features were selected using three feature selection methods: recursive feature elimination (RFE), principal component analysis method (PCA), and extreme trees (ET). The selected feature vectors were classified using support vector machines (SVM). Finally, a total of twelve subjects’ EEG data from Inner Mongolia University of Technology (IMUT data), the 2nd BCI competition in 2003, and the 4th BCI competition in 2008 were employed to show the effectiveness of this proposed FMCM-ETFS method. The results show that the classification accuracy using the multidimensional fused feature extraction (AR + CSP + DWT) is 3%–20% higher than those using the aforementioned three single feature extractions (AR, CSP, and DWT). Extreme trees (ET), which is a sort of tree-based model method, outperforms RFE and PCA by 1%–9% in term of classification accuracies, when these three methods were applied to the procedure of feature extraction, respectively.

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Li, Wu. "The Image Feature Extraction Algorithm Based on the DWT and the Improved 2DPCA." Applied Mechanics and Materials 556-562 (May 2014): 5042–45. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5042.

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The technology of 2DPCA is the feature extraction method proposed aiming at two-dimension image based on the traditional PCA algorithm. The paper proposed a improved weighting 2DPCA algorithm, combined with the two-dimension discrete DWT to handle the image, posing the new feature abstraction method, experiment improved that the new feature abstraction method can improve the target recognition efficiently compared with the original 2DPCA algorithm.

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Toufiq, Dalia Mohammad, Ali Makki Sagheer, and Hadi Veisi. "Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis." Bulletin of Electrical Engineering and Informatics 10, no. 5 (October 1, 2021): 2588–97. http://dx.doi.org/10.11591/eei.v10i5.3013.

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The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.

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Dadras Javan, F., F. S. Mortazavi, F. Moradi, and A. Toosi. "NEW HYBRID PAN-SHARPENING METHOD BASED ON TYPE-1 FUZZY-DWT STRATEGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 247–54. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-247-2019.

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Abstract. The purpose of image fusion is to combine two images from the same view in order to produce an image with more information and higher quality. In this paper, a panchromatic image with high spatial resolution and a low-resolution multi-spectral image having rich spectral information are fused together to produce a high-resolution multi-spectral image that heritage the characteristics of both initial images. For this purpose, a hybrid pan-sharpening method, called ‘Improved Fuzzy-DWT’ have been proposed based on the modification of the parameters existed in the latest version of Fuzzy-Wavelet algorithm, and then it was compared with some other algorithms such as PCA, Gram-Schmidt, Wavelet, and two of its hybrid derivatives called PCA-Wavelet and IHS-wavelet. The comparison was conducted using DIV, SSIM, SID, CC, DS, and QNR spectral and spatial quality assessment metrics. The obtained results demonstrate that the proposed hybrid algorithm has relatively better performance in comparison with the other mentioned pan-sharpening techniques in terms of both spectral and spatial qualities, regarding it was superior in terms of SID, DIV, SSIM, DS. From the computational cost standpoint, the proposed method has the most running time compared with the other methods.

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Hemalatha, C., and E. Logashanmugam. "Face Recognition Based on Dwt and PCA Feature Extraction Using Artificial neural network Classifier." Medico-Legal Update 18, no. 1 (2018): 531. http://dx.doi.org/10.5958/0974-1283.2018.00113.5.

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Mazaheri, Samaneh, Puteri Suhaiza Sulaiman, Rahmita Wirza, Mohd Zamrin Dimon, Fatimah Khalid, and Rohollah Moosavi Tayebi. "Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT." Computational and Mathematical Methods in Medicine 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/486532.

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Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

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Ponni alias Sathya, Sethuraman, and Srinivasan Ramakrishnan. "Non-redundant frame identification and keyframe selection in DWT-PCA domain for authentication of video." IET Image Processing 14, no. 2 (February 7, 2020): 366–75. http://dx.doi.org/10.1049/iet-ipr.2019.0341.

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Prucnal, Monika, and Adam G. Polak. "Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network." Metrology and Measurement Systems 24, no. 2 (June 27, 2017): 229–40. http://dx.doi.org/10.1515/mms-2017-0036.

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AbstractEEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.

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Li, Lizhao, Song Xiao, and Yimin Zhao. "Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization." Sensors 20, no. 19 (October 3, 2020): 5666. http://dx.doi.org/10.3390/s20195666.

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This paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply either self-adaptive dictionary (e.g., principle component analysis (PCA) dictionary and singular value decomposition (SVD) dictionary) or fixed dictionary (e.g., discrete cosine transform (DCT), discrete wavelet transform (DWT), and Curvelet) as the sparse basis, while single dictionary could not fully explore the sparsity of images. In this paper, a Hybrid NonLocal Sparsity Regularization (HNLSR) is developed and applied to image compressive sensing. The proposed HNLSR measures nonlocal sparsity in 2D and 3D transform domain simultaneously, and both self-adaptive singular value decomposition (SVD) dictionary and fixed 3D transform are utilized. We use an efficient alternating minimization method to solve the optimization problem. Experimental results demonstrate that the proposed method outperforms existing methods in both objective evaluation and visual quality.

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Attallah, Omneya, Muhammet Fatih Aslan, and Kadir Sabanci. "A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods." Diagnostics 12, no. 12 (November 23, 2022): 2926. http://dx.doi.org/10.3390/diagnostics12122926.

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Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.

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Sumathi, S., R. RaniHema Malini, and V. Thulasi Bai. "Efficient Face Recognition Method Using Multi Algorithm and Average Half Face." Asian Journal of Computer Science and Technology 1, no. 2 (November 5, 2012): 20–23. http://dx.doi.org/10.51983/ajcst-2012.1.2.1706.

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Face recognition has received much attention in recent years due to its many applications such as human computer interface, video surveillance and face image database management. It is a challenging technique due to under different lighting conditions, facial expressions and changes in head pose. Single class of feature is not enough to capture all the available information in face. Multi algorithm approach of face recognition improves the accuracy using feature level fusion. This paper proposes an efficient technique for identification of an individual by using Average Half Face (AHF). We propose feature fusion technique using Principal component Analysis (PCA) and Discrete Wavelet Transform (DWT). For classification, distance classifier is used. The proposed method was tested using the cropped extended Yale B database, where the images vary in illumination and expression. High recognition performance has been obtained by fusion of PCA and Wavelet features at feature level for average half face compared to full face.

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Journal articles on the topic 'PCA AND DWT'

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