This paper includes face detection and recognition with the help of morphological shared weight neural network. Face detection is achieved with the combination of morphological hit miss operation and edge detection. Feature extraction of face image is done using Principal Component Analysis. These features are used to train the neural network. The output of neural network is compared with datab...

The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2006) is an attractive approach to obtain sparse loadings in principal component analysis (PCA). S-PCA was motivated by reformulating PCA as a least-squares problem so that a lasso penalty on the loading coefficients can be applied. In this article, we propose new estimates to improve S-PCA in the...

In this work, an attempt has been made to analyze human femur radiographic bone images using sharpness features and learning models. The sharpness features are derived for the neck of the femur bone images to characterize the trabecular structure. The significant parameters are found using Independent component analysis (ICA) and Principal Component Analysis (PCA). The first three most signific...

This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop. The feature extraction i...

In recent years, as one of the biometric identification technology, palm-print identification has received many reseachers’ attention. To solve the key problem of palm-print recognition -feature extraction, we propose a new method, which based on wavelet transform and principal component analysis. In general, we use wavelet transform to deal with palm print images and extract high-dimensional w...

Both two dimensional principal component analysis and fisher linear discriminant analysis are successful face recognition algorithms. Recognition rate, time complexity can be improved by combining the two algorithms with the very powerful tool discrete wavelet transform. Experiments on the ORL face database show that the proposed method outperforms PCA, LDA, DWT+LDA algorithms in terms of recog...

Objectives : To develop an efficient algorithm for face and iris multimodal traits on ORL CASIA dataset to increase the performance rate decrease error of model. The main goal is Methods: proposed utilizes a fusion modalities using Stationary Wavelet Transform (SWT) Local Binary Pattern (LBP) techniques. Principal Component Analysis (PCA) applied reduce dimensionality each sample, improving eff...

A new satellite image contrast enhancement technique based on the Discrete Wavelet Transform (DWT) and Principal Component Analysis has been proposed. By the use of discrete wavelet transform, the input image decomposed into four frequency sub-bands and estimates the eigen values and eigen vectors (PCA) of the low–low subband image and reconstructs the enhanced image by applying inverse DWT. Th...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...