Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Sajjad, Muhammad; Nasir, Mansoor; Muhammad, Khan; Khan, Saleem; Jan, Zahoor; Sangaiah, Arun Kumar; Elhoseny, Mohamed; Baik, Sung Wook

doi:10.1016/j.future.2017.11.013

Cited by 144 publications

(58 citation statements)

References 29 publications

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“…They employed basic principal component analysis, linear discriminant analysis, and its variations for face detection and recognition. Sajjad et al [29] proposed a Raspberry-Pi-and cloud-assisted face recognition framework, which employed bag of words for the extraction of oriented Features from Accelerated Segment Test (FAST) and rotated Binary Robust Independent Element Features (BRIEF) points [30] from the detected face, and a support vector machine for the identification of suspects.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition at a Distance for a Stand-Alone Access Control System

Lee

Park

Yoo

et al. 2020

Sensors

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Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

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Section: Related Workmentioning

confidence: 99%

Face Recognition at a Distance for a Stand-Alone Access Control System

Lee

Park

Yoo

et al. 2020

Sensors

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“…Los experimentos mencionados han sido realizados sobre unidades de gran nivel de procesamiento, sin embargo, en el proyecto presentado, se muestra la idea de generar RNA distribuidas sobre elementos de bajo procesamiento, en est e caso la tarjeta Raspberry. En la literatura se tienen pocos experimentos relacionados a esta tarjeta de desarrollo, en [17], los autores afirman que Raspberry Pi tiene recursos limitados, por lo que la RNA se procesa y se entrena en la Nube, y la Raspberry sólo se encarga de obtener los datos relacionados al reconocimiento facial.…”

Section: Redes Neuronales Y Aplicaciones Distribuidasunclassified

Diseño de una red neuronal distribuida entre dispositivos Raspberry Pi conectados a Internet por medio de XMPP

Contreras¹,

Varela²,

Gudiño-Peñaloza³

2019

RCS

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Resumen. Actualmente se tienen diferentes esquemas de comunicación que buscan la interconexión de la mayor cantidad de dispositivos posibles, tal como el Internet de las Cosas, para así poder controlar actuadores u obtener datos desde diferentes puntos geográficos. Cuando se tienen situaciones tales como el estudio de los comportamientos ambientales, análisis de los comportamientos de consumo de individuos, o análisis de comportamientos poblacionales generales, es necesario obtener información de diferentes zonas y analizarlas en conjunto para obtener el resultado adecuado. Por este motivo, la generación de sistemas que puedan trabajar en conjunto desde distintas latitudes se vuelve un tema de estudio importante. En este trabajo se describirán diferentes modelos para la generación de una red neuronal distribuida, o segmentada, entre diferentes dispositivos conectados a Internet comunicándose mediante el protocolo de mensajería XMPP. Estos modelos logran identificar patrones de entrenamiento con datos provenientes desde distintas fuentes. El modelo utiliza tarjetas de desarrollo Raspberry Pi 3B+ para procesar y entrenar la red neuronal.Palabras clave: internet de las cosas, redes neuronales distribuidas, cómputo distribuido, Raspberry Pi, XM PP.Abstract. Currently there are different communication schemes that seek the interconnection of any kind of devices, such as the Internet of Things, in order to control actuators or obtain data from different geographical points. When there are situations such as the study of environmental behaviors, or analysis of general population behaviors, it is necessary to obtain information from different areas and analyze such data together to obtain the correct result. For this reason, the generation of systems that can work together from different latitudes becomes an important topic of study. In this paper we will describe different models for the generation of a distributed or segmented neural network between different de-197

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“…Thus, the analytic models (23) and (24) are validated only for relations (25) in the case of TrFNs C = (c 1 , c 0 , c 2 ) and D = (d 1 , d 0 , d 2 ).…”

Section: Synthesis Of the Computational Library Of Horizontal And Vermentioning

confidence: 99%

“…In particular, different algorithms of fuzzy control strategies in the embedded control systems with specific architectures can be successfully implemented based on such electronic devices as PLCs (Programmable Logic Controllers) [14][15][16][17][18] and reconfigurable FPGA (Field Programmable Gate Array) systems [19][20][21]. Moreover, a lot of advantages are in applications of the microcontroller Arduino [22][23][24] and the microprocessor Raspberry Pi [25,26] for the real-time fuzzy data computations and fuzzy information processing in diverse applications.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

Kondratenko

2018

Data

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This work focuses on fuzzy data processing in control and decision-making systems based on the transformation of real-timeseries and high-frequency data to fuzzy sets with further implementation of diverse fuzzy arithmetic operations. Special attention was paid to the synthesis of the computational library of horizontal and vertical analytic models for fuzzy sets as the results of fuzzy arithmetic operations. The usage of the developed computational library allows increasing the operating speed and accuracy of fuzzy data processing in real time. A computational library was formed for computing of such fuzzy arithmetic operations as fuzzy-maximum. Fuzzy sets as components of fuzzy data processing were chosen as triangular fuzzy numbers. The analytic models were developed based on the analysis of the intersection points between left and right branches of considered triangular fuzzy numbers with different relations between their parameters. Our study introduces the mask for the evaluation of the relations between corresponding parameters of fuzzy numbers that allows to determine the appropriate model from the computational library in automatic mode. The simulation results confirm the efficiency of the proposed computational library for different applications.

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Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Cited by 144 publications

References 29 publications

Face Recognition at a Distance for a Stand-Alone Access Control System

Face Recognition at a Distance for a Stand-Alone Access Control System

Diseño de una red neuronal distribuida entre dispositivos Raspberry Pi conectados a Internet por medio de XMPP

Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

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