Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN)

Kumar, K. Kranthi; Kasiviswanadham, Y.; Indira, D.V.S.N.V.; palesetti, Pushpa Priyanka; Bhargavi, Ch. V

doi:10.1016/j.matpr.2021.06.373

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…Kumar et al [19] had suggested a face recognition and criminal identification system utilizing a multi-task cascading network. This system will be capable of automatically distinguishing criminal faces.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Criminal Facial Image Using Multistage Progressive V-Net for Facial Recognition by Pixel Restoration

Beulah Benslet,

Parameswari

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Criminal activity is expanding exponentially in modern society, which leads towards a great concern about security issues. Facial recognition technology (FRT) is a powerful computer-based system that increasingly being used for recognize and match faces to solve crimes and investigations.OBJECTIVES: Due to poor image clarity and noisy pixels, the detection of criminal faces tends to be inaccurate. Hence, image enhancement techniques are required to recognize criminals with better accuracy. In the proposed model, a multistage progressive V-net based image quality enhancing technique is employed to improve accuracy.METHODS: The Convolutional Neural Network (CNN) for restoring images called MPRV-Net has three stages for a difficult balance between spatial data and highly contextualized information for image restoration tasks while recovering images.RESULTS: For image restoration tasks, including denoising, deblurring, and deraining, MPRV-Net has provided considerable performance benefits on a number of datasets. The suggested network is significant as it eliminates all three types of deviations using a single architecture. The proposed model's performance is tested using performance metrics such as accuracy, precision, recall, and specificity, obtaining 94%, 96%, 93%, and 95%.CONCLUSION: Thus, the proposed Multistage Progressive V-Net model for effectively improves the criminal Facial image for detecting criminals in public places with greater accuracy.

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Section: Related Workmentioning

confidence: 99%

Enhancement of Criminal Facial Image Using Multistage Progressive V-Net for Facial Recognition by Pixel Restoration

Beulah Benslet,

Parameswari

2024

ICST Transactions on Scalable Information Systems

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“…One of the advanced technologies remembered in the field of computer vision is face recognition. Face recognition is used in several areas, such as attendance systems [2], security detection [3], detection of drowsiness and fatigue [4], crimes from video surveillance [5], robotics [6], biometric security [7], deforestation early detection [8], CT scan images [9], and also SARS-CoV 2 disease [10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Face Mask Using Convolutional Neural Network

Imam Husni Al Amin,

Deva Ega Marinda,

Edy Winarno

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time.

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“…Recognizing and confirming criminals is the initial step toward criminal detention. The criminal recognition system automatically recognises faces in images or videos and then identifies the face using a criminal database (Bruce et al, 1999;Ma et al, 2015;Kumar et al, 2021). Its face recognition system consists of three modules image recognition, video recognition, and updating the databases.…”

Section: Introductionmentioning

confidence: 99%

“…All of these databases are linked via a unique identification number. As a result, Shown in Figure 1., it could potentially be separated into two moves face detection, and face recognition [13][14][15]. Face detection is a one-to-one approach that compares an input image to a face template to detect the presence of a face in it.…”

Section: Introductionmentioning

confidence: 99%

Futuristic study of a criminal facial recognition system using open-source images

Kumar¹,

Gupta²

2023

Preprint

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Face recognition provides an uncertainty challenge to criminal face identification. Thus, this recognition has many uses, from security to entertainment. Even if other bio-metric characteristics like iris and fingerprints are reliable, a person must be present to use them. Therefore, they are not the best tool for thieves to use for authentication. Anyone can recognize a criminal in an image by using face recognition technology in conjunction with a criminal database that contains the individual's personal information. In addition to needing great accuracy, a criminal recognition system also needs to be flexible to numerous variations in illumination, occlusion, maturity level, postures, etc. We use the Haar cascade and Histograms of oriented gradients (HOG) recognition learning systems discussed and contrasted in this study. We assessed models and HOG outperformed the criminal recognition system. We also go through several practical uses for this criminal recognition.

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Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN)

Cited by 14 publications

References 7 publications

Enhancement of Criminal Facial Image Using Multistage Progressive V-Net for Facial Recognition by Pixel Restoration

Enhancement of Criminal Facial Image Using Multistage Progressive V-Net for Facial Recognition by Pixel Restoration

Real-Time Detection of Face Mask Using Convolutional Neural Network

Futuristic study of a criminal facial recognition system using open-source images

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