A solution based on combination of RFID tags and

2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

A solution based on combination of RFID tags and facial recognition for monitoring systems Van-Dung Hoang*, Van-Dat Dang Quang Binh University Dong Hoi city, Vietnam * Corresponding: [email protected]

Tien-Thanh Nguyen Quang Binh Department of Science and Technology, Dong Hoi city, Vietnam [email protected]

Abstract— Nowadays, science and technology and the industrial revolution 4.0 are growing rapidly. The field of object recognition has achieved significant result, and applied in many important tasks such as security monitoring, surveillance systems, autonomous systems, human- machine interaction and so on. The intelligent system based on deep learning technique is being used extensively in today's life. A smart system brings to many benefits in living assistant systems. The contribution presents a solution based on combination of facial recognition and RFID (radio frequency identification) tags for the office checkup task in surveillance monitoring system (SMS). The SMS is constructed based on two main techniques to building intelligent systems which consist of face recognition technology and RFID tag recognition to monitor employee attendance when they are entering or leaving the office. In this system, the deep neural network is studied for face recognition. The system is connected to the SQL Server database to store the connection and ensure to synchronization is superior to the normal monitoring management systems. Keywords— surveillance monitoring system, RFID, deep neural network, facial recognition

I.

level is not high. Some other systems do not synchronize data, do not integrate the features in the inspection, monitoring, ensure security, security and usability or have high cost products, units aren't mastering the technology, it is difficult to develop the system because it depends on the service provider, so it has not been applied much. There are some researches on multiple device processing for extraction feature and recognition [6-7]. From that fact, we have proposed a solution that combines face recognition and RFID tags to build applications that control people and leave the workplace. Our goal is to research and develop intelligent control systems, overcome two main disadvantages are the flexibility and security of the fingerprint recognition system and magnetic card system. Research results will help to be proactive in mastering technology, thereby creating a system that is intelligent, safe and accurate.

INTRODUCTION

II. RFID SYSTEM OVERVIEW

In recent years, intelligence human identification and recognition systems have been developed and applied into several fields of intelligent systems such as security surveillance system, product inspection, automatic packing process, and other industry applications [1-4]. However, there are many challenges in the accuracy of recognition procedures such as various appearances and background, light conditions, occlusion, and especially consuming time for processing. In this paper, we are expected to deal with the problem of time processing consuming by the use RF information for personal identification and facial recognition reconfirm of identification. The state of the art for real-time processing in object detection has rapidly improved [3]. There are now a number of companies and scientists that research and apply some smart systems in the process of controlling people into agencies, companies or banks. The systems often use solutions such as fingerprint identification, magnetic tag recognition... Although, it has been applied in practice, but these systems still have many limitations in the operation. The fingerprint recognition system applied in some agencies, when employees entering the organ need to come fingerprint scan points, create inconvenience, not flexible in the verification process. This magnetic card recognition (RFID) system provides flexibility in the verification process, however, the security

A. RFID technique RFID (Radio Frequency Identification) is a technology that identifies objects by radio waves. This technology allows the identification of objects through radio transceivers, which can then monitor, manage or trace each object. An RFID system usually consists of two main components: an RFID chip containing information) and a reader that reads information on the chip [13]. RFID technology allows a device to read information stored in the chip without direct contact at a distance, without any physical communication. This technology provides a method for transmitting and receiving data from one point to another. RFID technology uses wireless communications in the radio frequency range to transmit data from the tag to the reader. Tags can be attached to identifiable objects such as products, boxes, or pallets. The reader scans the tag's data and sends the information to the database that stores the tag's data. Example, tags can be placed on a car windscreen so that road toll systems can quickly identify and collect money on routes. The simplest form being used today is a passive RFID system that operates as follows: The transmitter transmits the radio frequency signal from its antenna to a chip. The reader reads the information back from the chip and sends it to the reader

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Diem-Phuc Tran Duy Tan University Da Nang city, Vietnam [email protected]

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2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

control computer and processes the information obtained from the chip. The chips operate using the energy received from the signal sent by the reader. B. RFID components A simple RFID system is made up of two main components: device reader/writer and tag.

in the image is performed. At the end of the face detection, we obtained the alignment image from the input image data set. The detection process is performed continuously ensuring that all faces are found in the image.

Device reader/writer: It is a wireless communication device that can detect tags that have the same operating frequency within a certain range. The function of the readwrite device is used to read and write data stored in the magnetic card.

Fig. 1. Device reader/writer MFRC522 C. Operational principle Reader/writer device emits electromagnetic waves at a certain frequency through the antenna, when the magnetic card is in the broadcast area of the reader. It receives the energy and retransmits its own code. From there, it knows exactly which devices are in control area. Most RFID systems often have multiple read devices connected to a central computer. The central computer is responsible for receiving data from the reading device, analyzing, and executing commands related to the data stored in the tag [14].

Fig. 2. The operating principle of an RFID system III. FACE IDENTIFICATION USING DEEP LEARNING A. Data Collection and Processing In this section, we have collected face images by downloading image files from Facebook, Instagram, ... or by taking a picture directly. Describe the image from the face image data set used, as illustrated in Fig. 3.

Fig. 3. Face descriptions were taken from the facial image data set Once the facial images are cut from the image data, the images will be transferred to the preprocessor to perform noise filtering and image normalization. Complete preprocessing, face images transferred to the neural network training (CNN) [5, 15]. Here, it will be trained directly through the CNN model class. After training through the layers, we obtained the characteristic vectors, based on the characteristic vectors, transferred to the class training. Classification is performed, with each class being a set of face features for the object in that class. At the end of the classification process, the indicators and values obtained through the training process are stored in the training model file as the basis for the identification and extraction of the label of a person.

It is corresponding to the image file for each identity, the data is specified in the personal directory. Identity is set to be the same as the folder name of the image data set containing that person's face. B. Training Facial images were trained using deep neural networks, as shown in Fig. 4. This process is accomplished through the following processes: First, we need to provide an input image with a feedback image containing the face object. After collecting the input data set, the face detection process

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2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

Fig. 4. Facial image training uses deep neural networks

database using ODBC, this is how we use to link data during the update process. The image below describes our tools used in building this system.

IV. PROPOSAL APPROACH A. The method model based on RFID and facial recognition A general ideal of proposal method based on combination of RFID tags for personal identification and facial recognition task for reconfirm of identification. Some related works for fact-finding were presented in [8-9]. We have developed a solution that combines facial recognition and RFID tags, including the following components: Unit of reading: Block function reading information from the card. Camera: This block is used to capture employee faces. Facial Recognition: This block uses face detection technology to identify the face that is sent from the camera. Process Block: This block performs data reception from the R/W device, sends the ID and face images to the server block, and receives commands from the server and executes the commands associated with the output. Server block - this block receives and processes information from the send processing block, performing queries on the database. The image below is a diagram of a combination of facial recognition and RFID tags.

V. EXPERIMENTS For evaluation of the proposed approach, the training and evaluation image datasets were created by handmade processing. In our application situation, security cameras are mounted on high of system, therefore the training dataset is also generated in the same situation to realistic application. To accomplish this experiment, we used a device reader RFID tag MFRC522 and an Arduino Uno R3 circuit board. We used the Arduino tool to write code for the control chip on the RFID reader [13]. The RFID tag identifier is implemented on the Visual Studio tool and SQL server database. Facial identification was written on the Pycharm tool [10] and combined with Tensorflow open source libraries [11] and OpenCV [12]. The coding process is done on a personal computer under configuration of 2.2 GHz Core i5-5200 and 4GB RAM. The actual system connection model in the empirical process is illustrated in Fig. 8

Fig. 5. Combination of facial recognition and RFID tags B. System development To build this system, we wrote the embedded code for the Arduino Uno R3 circuit to retrieve the RFID tag chain and hardware device driver. IDs on RFID tags are processed, stored on SQL Server.

Fig. 7. Realistic model: Describing the components of the system that are connected in reality.

Fig. 6. Support tools for system development We build an employee management database through the ID code provided to each employee using RFID tags. For each employee, we collect face images to improve the security of the system. By applying deep learning techniques and CNN, we conduct training and acquire face IDs. We started processing face IDs and linking them to the SQL Server database. The method of linking to SQL Server

Fig. 8. The practical system The identification process is performed when the face detection system detects faces in the image sent from the surveillance camera. This system performs the extraction of

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2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

feature vectors on the face. Then, it proceeds to compare these feature vectors with the feature vectors stored in the previous facial model. The system identifies each worker's ID through a process of comparing feature vectors. The status ID will be updated continuously on the SQL Server database when the facial recognition process is complete. Facial recognition processes and RFID tags are implemented and updated continuously through the SQL Server shared database connection protocol. The image below is the system interface that we build for monitoring, manipulating and managing system activity, as illustrated in Fig 9.

IEEE International Conference on Computer Vision, pp. 1960-1967, Dec. 2013. [2] V.D. Hoang and K.H. Jo, "Joint components based pedestrian detection in crowded scenes using extended feature descriptors", Neurocomputing, 188, pp.139-150. [3] W. He, Y. Li, K. Chiew, T. Li and E. W. Lee (2011). A Solution with Security Concern for RFID-Based Track & Trace Services”, In Designing and Deploying RFID Applications, InTech, 2011. [4] D.P. Tran, V.D. Hoang, T.C. Pham, and C.M. Luong, "Pedestrian activity prediction based on semantic segmentation and hybrid of machines", Journal of Computer Science and Cybernetics, vol. 34(2), pp. 113-125, 2018. [5] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", preprint arXiv:1409.1556, 2014. [6] V.D. Hoang, M.H. Le, and K.H. Jo, "Planar motion estimation using omnidirectional camera and laser rangefinder", IEEE International Conference on Human System Interaction (HSI), pp. 632-636, 2013. [7] L. Kurnianggoro, V. D. Hoang, and K-H Jo. "Calibration of a 2D laser scanner system and rotating platform using a point-plane constraint", Computer Science and Information Systems, vol. 12(1), pp. 307-322, 2015.

Fig. 9. The system interface: Showing information of employee when they are coming to office, the system used RFID tags and face checking for verifying the employee TABLE 1. TEST THE ACCURACY OF FACIAL RECOGNITION USING DEEP LEARNING

Test no 1 2 3 4 5

Number of images 200 200 300 300 300

Positive

Negative

Accuracy

185 187 279 282 286

15 13 21 18 14

92,5% 93,5% 93,0% 94,0% 95,3%

[8] B.T. Nguyen, M.H. Trinh, T.V. Phan and H.D. Nguyen, “An efficient real-time emotion detection using camera and facial landmarks”, Seventh International Conference on Information Science and Technology (ICIST), pp. 251255, 2017. [9] F. Schroff, D. Kalenichenko, and J. Philbin. “Facenet: A unified embedding for face recognition and clustering”, In Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015.

After each test for each image is recognition incorrectly, we proceed to adjust the training data set. Results for the best test are 95.3%. VI. CONCLUSIONS In this article, we have presented a solution that combines facial recognition and RFID tag used in the human entering and leaving control system. This solution offers greater reliability than conventional solutions through two-step RFID tag verification and face recognition. The process of operating the system is stored in the SQL Server database management system to ensure the safety and synchronization. The experimental results showed that the effectiveness of the proposed method for speed up classification processing. REFERENCES [1] O. Barkan, J. Weill, L. Wolf, H. Aronowitz, "Fast high dimensional vector multiplication face recognition", Proc.

[10] Q. N. Islam, “Mastering PyCharm”, Packt Publishing Ltd, 2015. [11] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, "TensorFlow: A System for Large-Scale Machine Learning", In OSDI, vol. 16, pp. 265-283, 2016. [12] R. Laganière, “OpenCV Computer Vision Application Programming” Cookbook Second Edition, Packt Publishing Ltd, 2014. [13] S. Monk, “Programming Arduino Getting Start With Sketches”, McGraw-Hill,2011 [14] M. A. Nielsen (2013), “Neural Networks and Deep Learning”, Determination Press. [15] O. M. Parkhi, Andrea Vedaldi, Andrew Zisserman, “Deep Face Recognition”, British Machine Vision Conference, 2015

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