Indonesian Journal of Electrical Engineering and Computer Science Vol. 31, No. 3, September 2023, pp. 1551~1558 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v31.i3.pp1551-1558  1551 Journal homepage: http://ijeecs.iaescore.com Image classification using machine learning Debani Prasad Mishra 1 , Sanhita Mishra 2 , Smrutisikha Jena 1 , Surender Reddy Salkuti 3 1 Department of Electrical and Electronics Engineering, International Institute of Information Technology Bhubaneswar, Bhubaneswar, India 2 Department of Electrical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Odisha, India 3 Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Republic of Korea Article Info ABSTRACT Article history: Received Oct 21, 2022 Revised Apr 26, 2023 Accepted May 6, 2023 The objective of this paper is to implement different tools available in machine learning/artificial intelligence to classify faces and identify different features, highlights, and correlations or similarities between different celebrity faces which can apply in everyday security purposes to identity virtually if the authorized personnel is using certain access or not. The material present in this paper is a literature review of a machine learning model developed by the students. This is a classical problem of machine learning executed using a support vector machine. Images are separated based on sub-images. Each sub-image has been classified into a responsive class by an artificial neural network. The website then fetches the data from the back end and classifies the image into the corresponding personal. Keywords: Image classification JavaScript Machine learning Python flask Support vector machine This is an open access article under the CC BY-SA license. Corresponding Author: Surender Reddy Salkuti Department of Railroad and Electrical Engineering, Woosong University 17-2, Jayang-Dong, Dong-Gu, Daejeon-34606, Republic of Korea Email: surender@wsu.ac.kr 1. INTRODUCTION Machine learning (ML) by its definition is a field of computer science that was evolved from pattern recognition. It helps to learn and make algorithms of code that learns from and makes predictions based on the input data sets. Image classification is a method of visual processing that distinguishes between several categories of objectives based on image attributes. In pattern recognition and computer vision, it is commonly utilized. The support vector machine (SVM) is a new ML method that is based on statistical learning theory and has a solid mathematical foundation. It is based on the structural risk minimization model [1]. Various deep learning techniques for recognizing various images are described in [2]. The time spent at some commonly used image processing operations using OpenCV’s built-in central processing unit (CPU) and OpenCV’s built- in graphics processing unit (GPU) functions which are written with compute unified device architecture (CUDA) support is presented in [3]. A representation for checking the video structure alongside the device and improving it further using artificial intelligence (AI) methods such as SVM and artificial neural network (ANN) is proposed in [4]. Both techniques are used in tandem to train and test the classification to obtain results that are appropriate for the investigation and, as a result, proven to be successful. In the factual context, ML is characterized as the utilization of man-made brainpower where accessible information is utilized through algorithms to process or help the preparing of measurable information. While ML involves concepts of computerization, it requires human direction [5], [6]. AI involves an undeniable degree of speculation altogether to get a framework that performs well on yet inconspicuous information instances. ML is a moderately new control inside computer science that gives an assortment of information