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Palmprint is very popular biometric recognition system that is able to guarantee high accuracy. It has attracted increasing amount of attention because palmprints are abundant of many characteristics, such as the principle lines, ridges, minute points and textures for the use of images with low resolution. In this paper we propose palmprint feature detection based on KAZE technique. Palmprint texture has many important points for discrimination process. Selecting the best number of point using KAZE is very important for classification process in order to avoid overlapping features in different class. The experimental work has been done using polyU palmprint database in order to evaluate the best number of features.

<span>The multibiometric recognition system considered more reliable than the unimodal biometric recognition system due to the addition of an extra information that increases the discrimination between the classes. In this paper, a multi-sample multi-instance biometric recognition system is proposed. The aim of the proposed system is to increase the robustness of the identification. the proposed system also addresses the overfitting to the train samples problem of a feature extraction algorithm, named 2-Dimensional Linear Discriminant analysis (2D-LDA). The samples in the proposed method are bootstrapped and the 2D-LDA performed on each group during the offline phase. While in the online phase, the tested class will be transformed into subspaces using different eigenvectors that obtained from different samplings, and the results matched with templates in the corresponding subspace. To evaluate the proposed method, two palmprint databases are used which are IIT Delhi Touchless Palmprint Database and PolyU palmprint database, and different rank-level fusion algorithms are investigated. The results of the proposed method show improvement in the identification rate.</span>

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<span lang="EN-US">According to the world health organization, pneumonia was the cause for 14% of all deaths of children under 5 years old. A computer-aided diagnosis (CADx) system can help the radiologist in the detection of pneumonia in chest radiographs by serving as a second opinion. The typical CADx is based on transfer learning which is done by transferring the learning of feature extraction from one task with plenty of available data to a related task with a scarcity of data. This approach has two limitations which are first, blocking the transferred model from extracting the features that are singular to the new dataset as well as the inability to reduce the complexity of the original model. To address these drawbacks, we proposed a convolutional neural network (CNN) model with low complexity and three paths for feature extraction. The proposed model extracts three different types of features and concatenates them into one feature that provides a good representation for the classes. The proposed model was evaluated on a publicly available dataset. The results showed outperformance by the proposed model compared to the transfer learning models with recall 0.912±0.039, precision 0.942±0.029, F-beta score 0.93, and Cohen’s kappa score 0.740±0.008. </span>