The biometrics recognition has been paid more attention by people with the advancement of technology nowadays. The human ear is a perfect source of data for passive person identification. Ear seems to be a good candidate solution since ear is visible, their images are easy to take and structure of ear does not change radically over time. Ear satisfies biometric characteristic (universality, distinctiveness, permanence and collectability). In this paper we presented a new algorithm for ear recognition based on geometrical features extraction like (shape, mean, centroid and Euclidean distance between pixels). Firstly, we made a pre-processing phase by making all images have the same size. Then we used the snake model to detect the ear, and we applied median filter to remove noise, also we converted the images to binary format. After that we used canny edge and made some enhancement on the image, largest boundary is calculated and distance matrix is created then we extracted the image features.Finally, the extracted features were classified by using nearest neighbor with absolute error distance. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results and obtained over all accuracy almost 98%

In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

Privacy and Security has become an increasingly serious ýproblem for any biometric systems. Template protection, ýwhich mainly prevents from data loss and hacking the stored ýtemplates, is one of the most important issues when considering ýprivacy and security. Cancelable biometrics approach Scheme ýhas been proposed to address this problem. On the other hand ýthe symmetric hash functions might be used to increase the ýbiometric security level by making it is hard to attack the ýtemplate. In this paper, a new hybrid approach based on ýcombining approaches based on transformation and ýcryptosystem on fingerprints is applied. The results are ýcompared to a pre-presented approach in [1] showing that the ýnew approach is more efficient as the security of biometric ýtemplate is increased meanwhile the error rate is minimized.

Biometrics have lately been receiving attention in popular media. Biometrics deal with identification and verification of individuals based on their behavioral or physiological characteristics. Biometrics will become one of the most important ways of the identification technology. Ear recognition might be a good solution since ear is visible, ear images are easy to be taken, and the ear structure does not change radically over time. In this paper an algorithm based on SIFT features for ear recognition is proposed. SIFT key points are extracted from ear image and an augmented vector of extracted SIFT features are created for matching. Firstly, a pre-processing phase is done by converting image to gray level. Then a median filter is applied to smooth the image and to remove noise if found. Edge detection is used for cropping ear part from the image. Then the SIFT features were extracted from ear image. Finally, the extracted features were classified by using minimum distance classifier. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results compared with other researchers and obtained over all accuracy almost 95.2 % for IIT Delphi database and 100% for AMI database.