Home automation security system based on face detection and recognition using raspberry pi and python with RFID reader

To Usually, schools have an attendance system where they are recorded manually by the teachers. Later this data is entered into a computer and the aggregate percentage of student’s attendance is calculated. This method results in duplication of work and increases manpower requirements. Recently, student’s attendance has been considered as one of the crucial elements or issues that reflects the academic achievements and the performance contributed to any school authorities compared to the traditional methods that impose time-consuming and inefficiency. Raspberry Pi microprocessors can be used to interface with an RFID (Radio Frequency Identification) reader to save user information and time to an SD (Storage Device) card. The reader can also emit radio frequency signals that are received by RFID tags, which then transmit their unique identification numbers. RFID is a wireless technology which uses to a purpose of identifying and tracking an object via radio waves to transfer data from an electronic tag, called RFID tag or label to send data to RFID reader, then immediately web camera starts to recognise the face of the student and marks his/her attendance. This Raspberry Pi based project is for reducing manpower and implementing automation of attendance system and furthermore being beneficial for the schools or any other workplace.

Face detection entails the technology that identifies the human face in digital images. Face detection can be used in various applications. One of the major areas of application of face recognition is in security systems. We know that a smart security system is the need of the hour and has become a necessity in today’s time. It is the basic need of humans to be able to protect one’s home and belongings. The security system needs to be smart and efficient. Hence, it avoids intruders and protects your possessions by minimizing break-ins. Installing a home security system helps in protecting your home and valuables from burglars. The security system developed has a camera module that will give access to only authenticated persons to unlock the main door of the house using facial recognition technology. This discrete system is developed using an electronic board called as Raspberry Pi can detect intruders as well as send off an alarm to nearby police stations using SMS or Email. Keywords: Home security; Face Recognition; Internet of Things (IoT); RaspberryPi Microcontroller; Control; Smartphones Cite this Article Ayush Yadav, Shaam Garg, Shivam Jamloki, Aanchal Sadana, Kusum Tharani. Home Security using Facial Recognition. Current Trends in Signal Processing. 2020; 10(2): 5–11p.

최근 RFID (Radio Frequency Identification) 기술이 다양한 분야에서 관심을 받고 사용됨으로써 RFID 리더 제조회사들은 다양한 종류의 리더를 생산하고 있다. 현재 RFID 기술에서 RFID 태그 데이터 전송 형태는 EPCglobal에서 제공하는 표준 규약을 따르고 있지만 각 리더 별 리더 연결 및 통신 방법은 회사별로 다른 실정이다. RFID 리더를 사용하던 중 타 회사나 리더 인터페이스가 다른 RFID 리더로 변경하거나 새롭게 추가해야 하는 경우에는 사용자가 직접 RFID 미들웨어 개발단계로 돌아가 미들웨어의 내부 요소를 변경해야 하는 상황이 발생한다. 본 논문에서는 RFID 기술을 사용 시, 특히 여러 회사 또는 표준화되지 않은 인터페이스를 제공하는 RFID 리더를 사용하는 경우에 RFID 애플리케이션과 독립적으로 리더를 관리할 수 있는 리더 프레임워크를 개발하고자 한다. 본 리더 프레임워크는 RFID 애플리케이션과 RFID 리더 사이에 존재하여 RFID 리더 투명성을 제공한다. 본 리더 프레임워크는 현재 작동되고 있는 리더를 모니터링 할 수 있고, 리더의 속성을 확인할 수 있으며 RFID 태그 이벤트 정보, 시스템 로그 정보를 확인할 수 있다. 본 리더 프레임워크를 통해 수십 대의 이기종의 RFID 리더 상태를 용이하게 관리할 수 있게되며 애플리케이션 상의 코드 수정 없이 리더의 추가, 삭제가 가능하게 된다. More recently, variety RFID (Radio Frequency Identification) readers are produced by RFID equipment manufactures. Although a transmission standard instituted by EPCglobal is proposed for data transmission between the RFID readers and tags, other RFID reader protocols and the communication connection methods are be in use in other RFID companies. To replace or add the RFID readers of an RFID system, the developers should make changes to the core of the application and/or middleware. In this paper, we propose an RFID reader framework which can manage RFID readers without having to make changes the code of the application in environment with the growing number of heterogeneous RFID readers.This framework that sits on the layer between the RFID readers and the applications provides transparency to the RFID readers. Additionally, it can be used for monitoring the state and the property of all connected RFID, and for recording the RFID tag event logs and system logs. By using this framework, heterogeneous readers can be replaced and added without writing additional code in the applications. Consequently the readers can be easily managed and controlled by the RFID system administrator.

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However,these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.

Face recognition system has always been a crucial human-computer interaction tool in most security systems, access control, video surveillance, commercial areas, and networks like Facebook as well. After development in the area of artificial intelligence and growing security concerns, face recognition system has attracted attention by providing maximum efficiency in face detection and recognition. Face recognition is considered to be a biometric artificial intelligence-based application. Face recognition is the process of recognizing the face of a person by a vision system. As an extension to the existing face recognition system in this model, a new feature is added i.e., a voice message as output that is produced for each face recognized. The voice message could be anything like a simple hello or even a complex sentence. Keywords: Artificial intelligence, Face recognition, voice message, OpenCV, pyttsx.

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

From the past two decades, various spheres of activity are in the aspect of ‘Face recognition’ as an essential tool. The complete series of actions of face recognition is composed of 3 stages: Face Detection, Feature Extraction and Recognition. In this paper, we make an effort to put forth a new application of face recognition and detection in education. The proposed system scans the classroom and detects the face of the students in class and matches the scanned face with the templates that is available in the database and updates the attendance of the respective students.

In this paper, we focus on configuring a low cost RFID (Radio Frequency IDentification) system with less RFID readers and low density RFID tag textiles for stable and accurate self-localization for an omni-directional mobile robot. An RFID system using multiple RFID readers and high density RFID tags has already been applied to an indoor mobile robot self-localization. However, the system production cost is relatively high. To reduce the production cost of the system while maintaining the self-localization performance, we redesign two new RFID systems configured with 20 RFID readers and 5 RFID readers enlarging the size of an RFID reader antenna. Particularly, a new likelihood function of the tag detection with the redesigned large size RFID reader antenna is proposed for Monte Carlo Localization (MCL). Furthermore, we come up with a novel arrangement of RFID tags installed in a hexagon pattern. The new arrangement of ID tags enables both low cost ID tag installation and high performance of self-localization. We examine the performances of robot self-localization with less RFID readers and lower density RFID tags installed on the floor based on MCL(Monte Carlo localization). The simulation demonstrates the validity of our proposed configuration of the RFID based self-localization.

Radio Frequency IDentification (RFID) technology successfully integrates radio transmitter and receiver, small size memory space and control circuitry to remotely store and retrieve data via wireless RF transceiver. As low-cost RFID tag can be massively manufactured by semiconductor industry, new applications associated with RFID technology have emerged rapidly such as inventory tracking, book storage and management, and airport baggage handling. Generally an RFID system is composed of many RFID tags (or tagged objects), at least one RFID reader, and one backend application system with database. An RFID tag gains its operation energy from query signals that are radio waves transmitted by RFID reader. Once a tag responds the query with its stored information to RFID reader, usually a unique identity number or string, the RFID system can recognize the tag carrier and perform required business logic operation. As RFID technique is widely used in supply chain management, the corresponding RFID application systems are expected to process and manage large amount of tagged objects at a predefined period of time regularly. Therefore, for large-scaled RFID applications, tag reading throughput is very critical since it will affect the total data processing time. To measure tag reading throughput of a RFID system, two performance criteria are usually adopted: tag reading delay and reader energy consumption [5]. As RFID reader and tag always communicate over a shared wireless channel, it is very easy to have signal collisions during a normal tag identification process when multiple tags exist in reader’s interrogation area. Signal collision results in query or data retransmission and eventually increases communication overhead and time delay of tag identification. Hence, an efficient tag collision arbitration mechanism is very important and critical for RFID systems to achieve effective performance. Existing RFID tag identification schemes can be classified into aloha-based solutions [3, 10, 12-13, 16, 21, 23-24] and tree-based solutions [1, 2, 4, 6-9, 14-15, 17-20, 22]. In aloha-based schemes, a reader estimates the number of tags in its interrogation area and broadcasts the total number of timeslots to each tag. Then each RFID tag randomly selects its own timeslot to transmit its ID without knowing which timeslots have been selected by other tags. By distributing tag responses to distinct timeslots instead of getting all tag responses at the same time, aloha-based schemes effectively reduce the probability of signal collision. However, aloha-based schemes have tag starvation problem in which certain tags may not

<p>Visually impaired has the limitation in interacting with another human. They usually use the sense of hearing and touching their face to recognize human. Face recognition is a technology that can be used to solve this problem. This paper develops a smart cane function by integrated face recognition feature on the cane using Haar-Like features and Eigenfaces. This paper proposed a portable, real time, and wearable product. Raspberry Pi supports portability that affects the delay and computing speed of face recognition algorithms. Utilization of Raspi camera on the eyes glasses is for wearable purposes. Voice output provides information on whether the face is caught on camera or not. This prototype works well during the face detection and recognition process. It needs 3 seconds for one-face recognized in range 0.25 until 1.5 meters from the camera, until the sound and information are generated. It needs is me 5 second for two faces recognized and 10 seconds for 3 faces recognized by a system in the same range between face and the camera. The accuracy reaches 91.67% for the up-right position face but for other position the accuracy is only 18% until 32%.</p>

Real-time face detection and recognition using hybrid-information extracted from face space and facial features

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. Haar feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique. Key Words: Face Detection, MTCNN, Deep Learning, Real-Time Surveillance, Criminal Identification, CNN

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

Face recognition has numerous applications in the modern world. With recent developments in IoT devices, security, and biometric systems, many applications of face recognition are being used on devices like a raspberry pi. In this paper, we propose an efficient face recognition system that uses face detection and extraction of the face from image based on Single Shot Multibox Detector (SSD) and uses image enhancement techniques like bilateral filtering and histogram equalization to enhance the quality of face image after which Principal Component Analysis (PCA) is used for feature extraction and Linear Discriminant Analysis (LDA) is used as classifier. The experiments have been conducted on the Faces95 and Faces96 datasets to test the proposed system and the performance of the system is also compared with two other methods for face recognition namely LBPH and PCA with SVM classifier. The testing of the system in real-time shows great results while recognizing faces.