ISSN 2303-4521 Periodicals of Engineering and Natural Sciences Original Research Vol. 10, No. 2, April 2022, pp.500-511 © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal. 500 Descriptor feature based on local binary pattern for face classification Seba Aziz Sahy Middle Technical university, Institute of Medical Technology Al-Mansour, Iraq. ABSTRACT Local Binary Patterns (LBP) is a non-parametric descriptor whose purpose is to effectively summarize local image configurations. It has generated increasing interest in many aspects including facial image analysis, vision detection, facial expression analysis, demographic classification, etc. in recent years and has proven useful in various applications. This paper presents a local binary pattern based face recognition (LBP) technology using a Vector Support Machine (SVM). Combine the local characteristics of LBP with universal characteristics so that the general picture characteristics are more robust. To reduce dimension and maximize discrimination, super vector machines (SVM) are used. Screened and Evaluated (FAR), FARR and Accuracy Score (Acc), not only on the Yale Face database but also on the expanded Yale Face Database B datasets, the test results indicate that the approach is accurate and practical, and gives a recognition rate of 98 %. Keywords: Face Classifaction, False Reject Rate ,Local Binary Pattern, False Accept Rate ,Support Vector Machine. Corresponding Author: Seba Aziz Sahy Middle Technical university Institute of Medical Technology Al-Mansour, Iraq E-mail: saba.aziz@mtu.edu.iq 1. Introduction Faces are significant in our social lives since they allow us to learn about people's personalities, genders, ages, familiarity, and emotions. Humans recognize known faces more quickly and reliably than new faces, which is especially obvious in difficult viewing conditions where novel face classification frequently fails [1]. The development of biometric applications such as facial recognition has recently been a major priority in smart cities. Furthermore, numerous physicists and engineers all over the world have worked to design highly effective and powerful algorithms and procedures for these systems, which they now utilize in their daily lives. All types of security systems must secure personal data. The most frequent type of identification is a password. Many applications have begun to use a variety of biometric variables for the identification role because of the development of information technology and authentication algorithms [1,2,3,4]. People can be identified based on physiological or behavioral features thanks to these crucial variables. It also has several benefits, such as requiring only one person to stand in front of the sensor and removing the need for multiple keys or secret codes. Many biometric recognition systems, such as iris, fingerprint [5, and speech [6], are based on this principle, and the face to this effect has recently been published. Human identification systems that are biologically oriented are appealing because they are simple to use. The human face is made up of a variety of structures and features. It has become one of the most extensively used biometric identification technologies in recent years [7,8,9] due to its capabilities in a variety of applications and domains (surveillance, home security, border control, and so on). Consumers can already use face recognition as a recognition (identifier) outside of their phones, for example, at airports, sports stadiums, and concerts. Furthermore, because this technology does not require human contact to operate, people can be identified merely based on the photos captured by the camera. Furthermore, numerous biometric technologies developed for various types of investigations have great identification accuracy. However, it will be interesting to develop new biometric facial recognition solutions to satisfy the real-time restrictions. [9] Feature extraction and classification are the two primary stages of a face