2022 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP) Department of Electrical and Computer Engineering, Shahid Chamran University of Ahvaz, Iran, 23 & 24 February 2022 978-1-6654-1216-2/22/$31.00 ©2022 IEEE FFDR: Design and implementation framework for face detection based on raspberry pi Dhafer Alhajim Department of Computer Technical Engineering Technical Engineering college, The Islamic University, 54001 Najaf, Iraq idcte202006@iunajaf.edu.iq Gholamreza Akbarizadeh Department Of Electrical Engineering, Faculty Of Engineering, Shahid Chamran University Of Ahvaz, Ahvaz, Iran g.akbari@scu.ac.ir Karim Ansari-Asl Department Of Electrical Engineering, Faculty Of Engineering, Shahid Chamran University Of Ahvaz, Ahvaz, Iran. karim.ansari@scu.ac.ir Abstract— In today's world, we are surrounded by data of many types, but the abundance of image and video data available offers the data set needed for face recognition technology to function. Face recognition is a critical component of security and surveillance systems that analyze visual data and millions of pictures. In this article, we investigated the possibility of combining standard face detection and identification techniques such as machine learning and deep learning with Raspberry Pi face detection since the Raspberry Pi makes the system cost-effective, easy to use, and improves performance. Furthermore, some images of a selected individual were shot with a camera and a python program in order to do face recognition. This paper proposes a facial recognition system that can detect faces from direct and indirect images. We call this system FFDR, which is characterized by high speed and accuracy in the diagnosis of faces because it uses the Raspberry Pi 4 and the latest libraries and advanced environments in the Python language. Keywords— facial recognition, machine learning, deep learning I. INTRODUCTION The Raspberry Pi is a miniaturized, card-sized computer consisting of a conventional keyboard and mouse that connects to a computer display or TV. The most recent version of this little board includes certain new capabilities that make it capable of replacing desktop PCs [2]. The previous versions' one-size-fits-all approach is gone with the Raspberry Pi 4. It comes with one, two, or four gigabytes of RAM. (This is the first time a Pi with more than 1 GB of RAM has been available.) The additional RAM expands the Pi's capabilities, including the ability to execute software, but the Raspberry Pi 4 remains the same excellent small DIY gadget. The Raspberry Pi started as a hacker's dream: a low-cost, low- power, highly extensible, hackable PC that came in a bare circuit board form factor [4]. It has been used to power everything from scaled-down Mars rovers to millions of science and hacking day projects in schools throughout the world [5]. It was designed as a one-part instructional gadget and a one-part tinkering tool. Face detection, often known as facial recognition, is a computer technology that uses artificial intelligence (AI) to discover and recognize human faces in digital pictures. Face recognition technology may be used to offer real-time monitoring and tracking of individuals in a variety of sectors, such as security [6] and personal safety [7]. It has developed from basic computer vision approaches to breakthroughs in machine learning (ML) and associated technologies, with the consequence being continual performance gains. It currently serves as a crucial first step in a variety of vital applications, including face tracking, face analysis, and facial identification. Face recognition has a major effect on the application's ability to conduct consecutive tasks. Face detection aids in determining which sections of an image or video should be focused on in order to evaluate age, gender, and emotions by utilizing facial expressions in face analysis [8]. Face recognition software employs algorithms and machine learning to locate human faces within bigger pictures that frequently include non-face items, including landscapes, buildings, and other human body parts such as feet and hands [9]. Human eyes are one of the simplest features to identify, so face detection algorithms usually start there. After that, the algorithm could try to recognize the brows, mouth, nose, nostrils, and iris. Once the system has determined that a facial region has been discovered, it performs additional tests to ensure that it has recognized a face [3]. The algorithms must be trained on colossal datasets, including too many positive and negative images, to achieve precision. The algorithms' ability to detect whether or not there are faces in a picture and where they are increases as a result of the training [10]. Because of the variety of elements such as orientation, expression, posture, position, pixel values, and skin color, the existence of spectacles or facial hair, and changes in camera gain, lighting conditions, and picture resolution, detecting faces in figures can be difficult. Face identification using deep learning has advanced in recent years, with the benefit of considerably outperforming standard computer vision approaches [11]. Face recognition, in other words, goes beyond detecting the existence of a human face to discovering whose face it is. The process employs a computer application that takes a digital image of a person's face, often obtained from a video frame, and compares it to photos stored in a database. In this paper, we will learn how to conduct face recognition using OpenCV. To create our face recognition system, we'll first conduct face detection, then use machine learning to extract face embedding from each face, train a face recognition system on the embedding, and lastly use OpenCV to recognize faces in both images and video streams.