Face Recognition based Surveillance System Using FaceNet and MTCNN on Jetson TX2

Jose, Edwin; Greeshma, M. S.; Haridas, T. P. Mithun; Supriya, M. H.

doi:10.1109/icaccs.2019.8728466

Cited by 77 publications

(27 citation statements)

References 14 publications

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“…al. [Jose et al 2019], a MTCNN supera de forma consistente os métodos convencionais sofisticados em relação à confiabilidade do desempenho em tempo real. Este desempenho em tempo real é de grande importância em um sistema de vigilância.…”

Section: Multi-task Cascaded Convolutional Networkunclassified

Detecção de Máscara em Python Usando OpenCV e Deep Learning

Brito¹,

Santos²,

Filho³

2021

Minicursos Do XIV Encontro Unificado De Computação Do Piauí (ENUCOMPI) E XI Simpósio De Sistemas De Informação (SINFO)

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Section: Multi-task Cascaded Convolutional Networkunclassified

Detecção de Máscara em Python Usando OpenCV e Deep Learning

Brito¹,

Santos²,

Filho³

2021

Minicursos Do XIV Encontro Unificado De Computação Do Piauí (ENUCOMPI) E XI Simpósio De Sistemas De Informação (SINFO)

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“…The recognition accuracy of DeepID [14] proposed by Yi Sun of the Chinese University of Hong Kong on the LFW dataset reached 97.45%. In 2015, Google's FaceNet [15] was better than DeepID, and the accuracy of face recognition on the same face data set reached 99.63%. However, although ResNet cannot achieve such a high recognition rate in face recognition, its network has fewer parameters and faster training speed, which makes it also has a great advantage in the field of face recognition.…”

Section: Introductionmentioning

confidence: 99%

Research on Face Recognition Algorithm Based on Improved Residual Neural Network

Wang¹,

Hou²,

Yiting³

et al. 2021

ACIS

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The residual neural network is prone to two problems when it is used in the process of face recognition: the first is "overfitting", and the other is the slow or non-convergence problem of the loss function of the network in the later stage of training. In this paper, in order to solve the problem of "overfitting", this paper increases the number of training samples by adding Gaussian noise and salt and pepper noise to the original image to achieve the purpose of enhancing the data, and then we added "dropout" to the network, which can improve the generalization ability of the network. In addition, we have improved the loss function and optimization algorithm of the network. After analyzing the three loss functions of Softmax, center, and triplet, we consider their advantages and disadvantages, and propose a joint loss function. Then, for the optimization algorithm that is widely used through the network at present, that is the Adam algorithm, although its convergence speed is relatively fast, but the convergence results are not necessarily satisfactory. According to the characteristics of the sample iteration of the convolutional neural network during the training process, in this paper, the memory factor and momentum ideas are introduced into the Adam optimization algorithm. This can increase the speed of network convergence and improve the effect of convergence. Finally, this paper conducted simulation experiments on the dataenhanced ORL face database and Yale face database, which proved the feasibility of the method proposed in this paper. Finally, this paper compares the time-consuming and power consumption of network training before and after the improvement on the CMU_PIE database, and comprehensively analyzes their performance.

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“…In most cases, cameras are usually installed in a fixed position, but in order to realize smart monitoring systems, a highly accurate embedded system and a large computing device that can work properly under low power consumption circumstances are needed. In recent decades, the development of science and technology has advanced rapidly, and large demands have arisen in the surveillance industry [1]. As a result, multi-camera systems have become widely used for surveillance and access control under various situations in the hopes of deterring crimes and improving both public and private security.…”

Section: Introductionmentioning

confidence: 99%

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization

Deng

Chiang

2020

Sensors

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Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of artificial intelligence and big data. FaceNet was presented in 2015 and is able to significantly improve the accuracy of face recognition, while also being powerfully built to counteract several common issues, such as occlusion, blur, illumination change, and different angles of head pose. However, not all hardware can sustain the heavy computing load in the execution of the FaceNet model. In applications in the security industry, lightweight and efficient face recognition are two key points for facilitating the deployment of DL and CNN models directly in field devices, due to their limited edge computing capability and low equipment cost. To this end, this paper provides a lightweight learning network improved from FaceNet, which is called FN13, to break through the hardware limitation of constrained computational resources. The proposed FN13 takes the advantage of center loss to reduce the variations of the between-class features and enlarge the difference of the within-class features, instead of the triplet loss by using FaceNet. The resulting model reduces the number of parameters and maintains a high degree of accuracy, only requiring few grayscale reference images per subject. The validity of FN13 is demonstrated by conducting experiments on the Labeled Faces in the Wild (LFW) dataset, as well as an analytical discussion regarding specific disguise problems.

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Face Recognition based Surveillance System Using FaceNet and MTCNN on Jetson TX2

Cited by 77 publications

References 14 publications

Detecção de Máscara em Python Usando OpenCV e Deep Learning

Detecção de Máscara em Python Usando OpenCV e Deep Learning

Research on Face Recognition Algorithm Based on Improved Residual Neural Network

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization

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