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), Naïve 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.

生物识别系统包括用于根据个人的行为和生理特征识别个人的算法，过程和设备。广泛使用计算智能（CI）的方法来建立基于生物特征的身份，并克服样本中通常存在的非理想性。本文的目的是分析和评估基于生物识别技术的各种用于人类识别的计算智能（CI）方法。该研究包括8种顶级CI算法，即：k最近邻（K-NN），人工神经网络（ANN），支持向量机（SVM），模糊判别矩阵（FDM），朴素贝叶斯（NB），k均值，决策树（DTs）和遗传算法（加油站）。研究还提供了这些算法的技术特点和特点，并找出了这些方法的优缺点。可以根据工作中呈现的数据的数量和质量来选择分析的算法。