Comparative study for 8 computational intelligence algorithms for human identification

The biometric system includes the algorithms, procedures, and devices which are utilized for the purpose of recognizing individuals according to their behavioral and physiological features. The approaches of Computational Intelligence (CI) are utilized extensively to establish biometric-based identities as well as overcoming non-idealities usually exist in samples. The objective of this paper is to analyze and evaluate the various computational intelligence (CI) approaches for the human identification based on biometrics . The study includes 8 top CI algorithms, namely; k-Nearest Neighbor(K-NN), Artificial Neural Networks (ANNs), Support vector machines (SVMs), Fuzzy Discernibility Matrix (FDM), Naive Bayes (NB), k-means, Decision Trees (DTs), and Genetic algorithms (GAs). Also the study provides the technical characteristics and features of these algorithms as well as finds advantages and disadvantages of these methods . The analyzed algorithms can be selected according to quantity and quality of data presented at work.