In today's world, keeping people healthy and safe requires smart technology. This research introduces a strong system that can detect if someone is wearing a face mask in real-time. It uses advanced computer vision and deep learning techniques. The main goal of this system is to quickly tell if someone in a live video is wearing a mask or not. The heart of the system is a deep learning model based on ResNet. It's been carefully trained to recognize facial features and accurately tell if someone is wearing a mask. This model works really well, reliably spotting people following mask rules and those who aren't. Additionally, the system can estimate the gender and age of people, which adds useful information. To make it easy for users to interact with the system, a Graphical User Interface (GUI) has been created using the Tkinter framework. This simple interface lets users easily use the system, seeing real-time results of mask detection.
Keywords: Face mask detection, Computer vision, Deep learning, Real-time classification, ResNet, Gender classification.
In today's world, security and safety are paramount concerns in various real-world scenarios, such as surveillance, law enforcement, and public safety. One critical aspect of ensuring security is the ability to monitor and enforce compliance with safety regulations, including the use of personal protective equipment (PPE) such as face masks. The development of automated systems for face mask detection has become increasingly important in these contexts as they offer practical solutions for enhancing security measures.
This project focuses on the development of a real-time face mask detection system using ResNet-based deep learning for accurate mask classification. The system is designed for deployment in environments where ensuring compliance with safety regulations is critical, such as airports, hospitals, and public transportation. By automating the process of monitoring mask-wearing behavior, the system aims to improve security and safety standards in these settings. The system's user-friendly interface, developed using Tkinter, allows for easy interaction and integration with existing security systems. Additionally, the system provides gender and age estimation for detected faces, which can be valuable for demographic analysis and profiling. Overall, this project addresses the need for effective security solutions in various real-world scenarios, contributing to the enhancement of public safety and security standards.
Arora et al. (2023) delve into computer vision, focusing on object detection, age estimation, and gender estimation using deep learning. Their study introduces a comprehensive framework utilizing advanced models like Mask R-CNN and the Deep Face library. By integrating these components, the research demonstrates precise object detection and nuanced age and gender estimations. The authors highlight their model's efficiency through meticulous information processing, resulting in high-quality output images. Rigorous testing validates the accurate identification of objects and precise estimation of age and gender, advancing computer vision and deep learning applications.
Krishnakumar et al. (2022) responds to the critical need for effective measures in combating COVID-19 and safeguarding public health. Their study focuses on developing precise techniques for detecting non-compliance with mask-wearing protocols in public areas. Leveraging MobileNet V2 as a foundation and utilizing transfer learning methods, the researchers enhance the model's accuracy in mask recognition. By integrating the Caffe Model with OpenCV's DNN module for face detection, their anticipated model exhibits exceptional performance suitable for real-time applications, particularly in live video surveillance systems for monitoring mask adherence.
Banati et al. (2022) present a pioneering approach to integrating soft biometrics and deep learning techniques to recognize facial features in masked images. Their study addresses the pressing need for efficient identification methods amidst the COVID-19 pandemic. They introduce a novel system that leverages ocular and forehead regions to detect soft biometric attributes like eyeglasses, hair type, and mustache. Employing transfer learning techniques, they achieve impressive accuracy rates, even with facial masks present. Their comparisons between masked and unmasked face images demonstrate the effectiveness of the proposed system, particularly highlighting the enhanced accuracy of MobileNet. This research significantly advances facial recognition technologies, particularly in overcoming challenges posed by mask mandates during the pandemic.
Wang, L., Zhang, Q., Chen, Y (2020) Focusing on privacy concerns related to facial recognition, this research investigates methods for enhancing privacy protection in facial recognition systems. The study explores approaches such as facial obfuscation and privacy-preserving feature extraction, providing insights into strategies for mitigating privacy risks in facial recognition technology.
Patel, K., Lee, J., and Smith, A. (2019) provide insights into facial recognition systems for security applications. The paper discusses traditional methods alongside recent advancements in deep learning, examining techniques, challenges, and ethical considerations associated with facial recognition technology.
Gupta, A., Singh, R., and Sharma, S. (2021) investigate real-time face mask detection using YOLOv3 and transfer learning. Their study aims to fine-tune pre-trained YOLOv3 models for efficient monitoring of mask compliance in various settings.
Williams, E., Johnson, M., & Brown, K. (2021) delve into the development of AI-based systems for face mask detection in public spaces. Their research explores the integration of machine learning algorithms with IoT devices to enhance real-time monitoring of mask compliance, aiming to contribute to public health efforts amidst the COVID-19 pandemic.
Chen, H., Li, W., & Liu, J. (2018) present a comprehensive review of deep learning techniques for facial recognition and attribute analysis. Their study evaluates the performance of various deep learning architectures and discusses challenges and future directions in the field of facial recognition and attribute prediction.
Garcia, P., Martinez, L., & Rodriguez, A. (2020) investigate the application of facial recognition technology in healthcare settings. Their research explores the potential of facial recognition systems for patient identification, access control, and personalized healthcare delivery, emphasizing the importance of accuracy, security, and privacy in healthcare-related applications of facial recognition.
Jones, R., Smith, T., & Patel, A. (2023) explore the use of machine learning algorithms for analyzing compliance with face mask mandates in public settings. Their study investigates the effectiveness of various deep learning models in accurately detecting individuals wearing masks and provides insights into the deployment of such systems for enhancing public safety measures.
The proposed system aims to augment existing mask detection capabilities by integrating age and gender prediction functionalities, thus offering a comprehensive solution for real-time monitoring of mask compliance. Leveraging ResNet for precise mask and face detection, supplemented by pre-trained models for age and gender classification, the system is poised to significantly enhance the efficacy of mask detection processes.
By incorporating ResNet for face and mask detection, the proposed system anticipates achieving superior accuracy and performance compared to existing approaches. The utilization of ResNet, a robust deep learning architecture, enables the system to effectively identify and classify facial features, thereby facilitating more precise mask detection.