Real-Time Face Recognition System for Remote Employee Tracking

• URL: http://arxiv.org/abs/2107.07576v1
• Date: Thu, 15 Jul 2021 19:21:37 GMT
• Title: Real-Time Face Recognition System for Remote Employee Tracking
• Authors: Mohammad Sabik Irbaz, MD Abdullah Al Nasim, Refat E Ferdous
• Abstract summary: To mitigate the spread of deadly coronavirus, many offices took the initiative so that the employees can work from home. To deal with the challenge effectively, we came up with a solution to track the employees with face recognition. In this paper, we discuss in brief the system we have been experimenting with and the pros and cons of the system.
• Score: 0.483420384410068
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: During the COVID-19 pandemic, most of the human-to-human interactions have been stopped. To mitigate the spread of deadly coronavirus, many offices took the initiative so that the employees can work from home. But, tracking the employees and finding out if they are really performing what they were supposed to turn out to be a serious challenge for all the companies and organizations who are facilitating "Work From Home". To deal with the challenge effectively, we came up with a solution to track the employees with face recognition. We have been testing this system experimentally for our office. To train the face recognition module, we used FaceNet with KNN using the Labeled Faces in the Wild (LFW) dataset and achieved 97.8% accuracy. We integrated the trained model into our central system, where the employees log their time. In this paper, we discuss in brief the system we have been experimenting with and the pros and cons of the system.

Related papers

• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework [7.6849475214826315]
  We propose a self-monitoring framework that allows for the perception system to perform plausibility checks at runtime. We show that by incorporating an additional component for detecting human body parts, we are able to significantly reduce the number of missed human detections.
  arXiv  Detail & Related papers  (2023-07-10T12:59:30Z)
• ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning [91.58711082348293]
  Reinforcement learning from online user feedback on the system's performance presents a natural solution to this problem. This approach tends to require a large amount of human-in-the-loop training data, especially when feedback is sparse. We propose a hierarchical solution that learns efficiently from sparse user feedback.
  arXiv  Detail & Related papers  (2022-02-05T02:01:19Z)
• Masked Face Recognition Challenge: The InsightFace Track Report [79.77020394722788]
  During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to deep face recognition. In this workshop, we focus on bench-marking deep face recognition methods under the existence of facial masks.
  arXiv  Detail & Related papers  (2021-08-18T15:14:44Z)
• Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection [3.04585143845864]
  unsupervised approach for detecting real-world, high-stakes deception in videos without requiring labels. This paper presents our novel approach for affect-aware unsupervised Deep Belief Networks (DBN) In addition to using facial affect as a feature on which DBN models are trained, we also introduce a DBN training procedure that uses facial affect as an aligner of audio-visual representations.
  arXiv  Detail & Related papers  (2021-08-17T22:07:26Z)
• A Computer Vision System to Help Prevent the Transmission of COVID-19 [79.62140902232628]
  The COVID-19 pandemic affects every area of daily life globally. Health organizations advise social distancing, wearing face mask, and avoiding touching face. We developed a deep learning-based computer vision system to help prevent the transmission of COVID-19.
  arXiv  Detail & Related papers  (2021-03-16T00:00:04Z)
• Multi-Stage CNN Architecture for Face Mask Detection [0.0]
  We introduce a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a dual-stage Convolutional Neural Network (CNN) architecture capable of detecting masked and unmasked faces. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.
  arXiv  Detail & Related papers  (2020-09-16T12:23:21Z)
• Perceiving Humans: from Monocular 3D Localization to Social Distancing [93.03056743850141]
  We present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule.
  arXiv  Detail & Related papers  (2020-09-01T10:12:30Z)
• Toward Building Safer Smart Homes for the People with Disabilities [1.0742675209112622]
  "SafeAccess" is an end-to-end assistive solution to build a safer smart home by providing situational awareness. We focus on building a robust model for detecting and recognizing person, generating image descriptions, and designing a prototype for the smart door. The system notifies users with an MMS containing the name of incoming persons or as "unknown", scene image, facial description, and contextual information. Our system identifies persons with an F-score 0.97 and recognizes items to generate image description with an average F-score 0.97.
  arXiv  Detail & Related papers  (2020-06-10T15:50:32Z)
• Smart Attendance System Usign CNN [0.0]
  We are introducing a smart and efficient system for attendance using face detection and face recognition. This system can be used to take attendance in colleges or offices using real-time face recognition. The attendance records will be updated automatically and stored in an excel sheet as well as in a database.
  arXiv  Detail & Related papers  (2020-04-22T09:04:33Z)
• Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification [93.5538147928669]
  We audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present.
  arXiv  Detail & Related papers  (2020-01-09T15:50:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.