3 research outputs found
Multispectral palmprint recognition based on three descriptors: LBP, Shift LBP, and Multi Shift LBP with LDA classifier
Local Binary Patterns (LBP) are extensively used to analyze local texture
features of an image. Several new extensions to LBP-based texture descriptors
have been proposed, focusing on improving noise robustness by using different
coding or thresholding schemes. In this paper we propose three algorithms
(LBP), Shift Local Binary Pattern (SLBP), and Multi Shift Local Binary Pattern
(MSLBP),to extract features for palmprint images that help to obtain the best
unique and characteristic values of an image for identification. The Principal
Component Analysis (PCA) algorithm has been applied to reduce the size of the
extracted feature matrix in random space and in the matching process; the
Linear Discriminant Analysis (LDA) algorithm is used. Several experiments were
conducted on the large multispectral database (blue, green, red, and infrared)
of the University of Hong Kong. As result, distinguished and high results were
obtained where it was proved that, the blue spectrum is superior to all spectra
perfectly
A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification
Brain cancer classification is an important step that depends on the physician's knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min–max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique
An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images
Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used