西南交通大学学报 第 54 卷第 6 期 2019 年 12 月 JOURNAL OF SOUTHWEST JIAOTONG UNIVERSITY Vol. 54 No. 6 Dec. 2019 ISSN: 0258-2724 DOI：10.35741/issn.0258-2724.54.6.43 Research article Computer and Information Science COMPARISON BETWEEN PHYSIOLOGICAL AND BEHAVIORAL CHARACTERISTICS OF BIOMETRIC SYSTEM Azal Habeeb a a University of Thi-qar, Department of Computer, Nasiriyah, Iraq. E-mail: azal.alamery2@gmail.com Abstract Biometrics is a technical aspect to identify each person from others. It is one of the ways to distinguish a person’s identity. The biometric system plays a vital role in data security. There are two types of biometric systems, i.e., physiological and behavioral biometrics. Physiological biometrics involves the fingerprint, iris, and face, while behavioral biometrics includes the signature, stroke, and voice. This paper discussed the iris recognition technique using the Canny edge detector and Hough transform to separate iris region from the eye images. The voice recognition technique was discussed using mel- frequency cepstral coefficient (MFCC) method. Finally, the paper compared iris recognition and voice recognition according to their properties and their performance. Keywords: iris recognition, voice recognition, biometric, segmentation, mel-frequency cepstral coefficient (MFCC) 摘要 生物识别技术是识别每个人彼此的技术方面。 这是区分一个人身份的方法之一。 生物识别系统 在数据安全中起着至关重要的作用。 有两种类型的生物识别系统，即生理和行为生物识别。 生 理生物特征包括指纹，虹膜和面部，而行为生物特征包括签名，中风和声音。 本文讨论了使用 Canny 边缘检测器和 Hough 变换将虹膜区域与眼睛图像分开的虹膜识别技术。 讨论了语音识别技 术，采用了密频倒谱系数（MFCC）方法。 最后，本文根据虹膜识别和语音识别的特性和性能进行 了比较。 关键词: 虹膜识别，语音识别，生物识别，分割，梅尔频率倒谱系数 I. INTRODUCTION Regarding personal identification, traditional methods rely on changeable parameters, such as passwords or magnetic cards. These parameters can be easily stolen by others. This method has a lot of disadvantages, such as forgetting, losing, and stealing, and also the card can be cracked because of those disadvantages. We choose the biometric system for the traditional human identification method. Concerning personal identification, biometric systems are more secure and safer than traditional methods. Biometrics has recently been attracting more attention in mass media. It deals with the identification of individuals based on their physiological or behavioral characteristics. Moreover, it is widely thought that biometrics can become an important component of the identification technology Biometrics can be divided into physiological and behavioral characterization [1]. The physiological biometrics involves the iris, fingerprint, palm print, ear, face, etc., and the behavioral biometrics includes gait, handwritten signature, keystroke voice, and human walking [2-5].This paper investigates the