Face Recognition from Video using Deep Learning

Manna, Saibal; Ghildiyal, Sushil; Bhimani, Kishankumar

doi:10.1109/icces48766.2020.9137927

Cited by 26 publications

(8 citation statements)

References 12 publications

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“…The system would have to be retrained each time a new user is to be added, and multiple image examples collected. Saibal Manna et al [24] proposed a face recognition system that makes searching for criminals easy and quick with less time. This research made use of a pre-trained FaceNet [29] model to transform input images into a close-packed Euclidean space.…”

Section: B Face Recognition Based Systemsmentioning

confidence: 99%

Edge AI Face Recognition for Public Transport Fare Payment

Rusoke¹,

Musinguzi²,

Miyingo³

et al. 2022

Preprint

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<p> Face Recognition technology is chiefly concerned with accurately re-identifying individuals through the use of mathematical face representations. It presents a window of opportunity for the introduction of a fast, automated seamless and easy-to-deploy form of biometric technology. In this research, we design a fast,easy-to-use, and privacy-oriented contactless payment system for public transportation that chiefly makes use of face recognition and internet of things technologies. We demonstrate a one-shot face recognition model and also prepare and test it for real-time inference on the edge. Our system makes use of a Siamese Model built on top of the Inception-Resnet V1 architecture with accuracy, precision and recall values of 93.81%, 90.91% and 97.35% on our validation set. The model was deployed on a Raspberry Pi 4 Model B with an Intel Neural Compute Stick 2. Inference was performed through the inference engine API of the OpenVINO toolkit on the Neural Compute Stick plugged into the Raspberry Pi. The system is composed of three other subsystems, i.e. the edge device, cloud database and user interface subsystems which work together to ensure that payment is complete in under 2 seconds. </p>

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Section: B Face Recognition Based Systemsmentioning

confidence: 99%

Edge AI Face Recognition for Public Transport Fare Payment

Rusoke¹,

Musinguzi²,

Miyingo³

et al. 2022

Preprint

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show abstract

“…Saibal Manna et al, [5] introduced a face recognition system that enables scanning for faces simply and fast by decreasing the time required in the procedure and to successfully help to recognize faces. Face recognition is performed using the OpenCV and FaceNet model models, which uses a deep neural network architecture to map faces from a Euclidean space.…”

Section: Related Workmentioning

confidence: 99%

Smart Surveillance System Using Face and Optical Character Recognition for Secure Environment

HarikaPalivela

Kumar

Krishna³

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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One of the important applications in the smart surveillance system is to identify unknown suspicious persons. Instead of manually and tediously monitoring the cameras continuously, this system can be used to identify and recognize suspected individuals and consequently send a warning message on the occurrence of such recognition. In this work, we propose a novel methodology, in which the system is connected to a database containing images of the Aadhar cards of the malfunctioning individuals. Since SQL database cannot directly store images, the Aadhar card pictures will have to be stored in a NoSQL database. The proposed system was built utilizing the Python language and the PyCharm IDE. The input video stream is processed frame by frame using the OpenCV library. The system will process each frame to check for the presence of a face. If a face is found, it matches the detected face with each of the faces present in the Aadhar card photos stored in the connected database. This process is carried out using a face recognition algorithm. If a match is found, the system uses OCR to isolate and recognize the text present in the image in order to get the name and Aadhar card number of the offender, and subsequently an alert will be displayed to the corresponding moderators with the name and Aadhar card number of that individual, in order to facilitate the necessary action.

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“…By the rapid development and advancement in machine learning techniques, the dead-end of face detection systems seems to be largely addressed and that may lead the communities to the effective detection and diagnosis of this disease and also, be helpful in addressing the public problems. In the last decade, a sub-field of machine learning called deep learning algorithms have achieved many striking developments in various fields of computer vision such as image classification [2]- [4], object detection [5]- [8], segmentation [9], [10], and face recognition [11]- [15]. While traditional face recognition techniques require manual feature extraction, deep learning algorithms do not include such kinds of procedures.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

Kocacinar¹,

Tas

Akbulut

et al. 2022

IEEE Access

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Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, currently, there are not enough image datasets that contain a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application were developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals' 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.

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Face Recognition from Video using Deep Learning

Cited by 26 publications

References 12 publications

Edge AI Face Recognition for Public Transport Fare Payment

Edge AI Face Recognition for Public Transport Fare Payment

Smart Surveillance System Using Face and Optical Character Recognition for Secure Environment

A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

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