Crowding Sourcing and Pattern Recognition for Identification and Sharing of Thief’s Information

Proceedings of ‏The 11th International Conference on Research in Engineering, Science & Technology

Year: 2021


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Crowding Sourcing and Pattern Recognition for Identification and Sharing of Thief’s Information

Marcelo Felix Assis de Fonseca; Marcus Vinicius Fonseca Trindade, Luiz Melk de Carvalho, Diva de Souza e Silva Rodrigues, Flávio Henrique Batista de Souza



Security solutions based on facial recognition are widely employed. However, due to the lack of information and appropriate structures, this technology does not benefit small and medium-sized Brazilian businesses (the victim of most criminal occurrences). This research demonstrates a solution focused on this audience and based on: affordable hardware interconnection; Multilayer Perceptrons (MLP); Cloud Computing (CC) and Crowdsourcing (CS). The main structure was developed with: Python programming language; MySQL as database management system; OpenCV (Open Source Computer Vision Library) to perform the real-time detection of the faces of the people who are entering the establishment. The images are recorded and later analyzed by a second algorithm responsible for returning an image vector. This vector is compared with the others vectors already registered in a database with the suspects, returning the percentage of similarity between them. A result higher than 70% enable an alert to the manager of the establishment, discreetly, for later consultations of the owners, or by judicial order. The structure works in CC and the filling of the database is done via CS by the establishments. Experiments with MLPs were performed to optimize the recognition process, considering 5 types of MLPs (Backpropagation Standard, Momentum, Weight Decay, Quick propagation; Resilient Propagation); 50 to 500 epochs, 7 to 10 neurons, learning rate of 0.01%, 25-35% validation and 75-65% training. It was performed 155 training processes (total: 19 hours and 31 minutes of test execution time). A maximum accuracy of 94% was reached. The solution can be implemented and integrated to the available Google cloud services.

Keywords: Multilayer Perceptron; Crowdsourcing; Cloud Computing; Security Structures; Facial Recognition.