Special Issue(24-18): Real-Time Face Detection Algorithms for Internet of Things (IoT) Applications | International Journal of Automation and Smart Technology

Real-Time Face Detection Algorithms for Internet of Things (IoT) Applications

Personalized customer interactions in stores and improved security in smart homes are just two examples of the many Internet of Things (IoT) applications that depend heavily on real-time facial identification. This technique utilizes the computing power of Internet of Things (IoT) devices for seamless data collecting and analysis. It includes the real-time detection and accurate location of human faces inside photos or video streams. Real-time face detection improves Internet of Things applications by enabling personalized user interactions, improving security by instantly recognizing individuals, automating smart home and office automation by identifying residents or staff, helps to monitor patient conditions and emotions in the healthcare industry, and enhancing customer analytics and personalized marketing in the retail sector. In real-time face detection, cameras or other sensors are used to capture images or video streams. Noise reduction, normalization, and resizing are then applied to improve image quality.

Many techniques and tools are available for real-time face detection in Internet of Things applications such as Haar Cascade, which uses the AdaBoost learning algorithm for efficient and fast face detection. The gradient orientation histograms are computed via Histogram of Oriented Gradients (HOG) in order to extract features. Applications like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) assist significantly from the high accuracy and speed of convolutional neural networks (CNNs). MTCNN (Multi-Task Cascaded Convolutional Networks) integrates many tasks to improve detection performance. Current developments in real-time face recognition for the Internet of Things involve edge computing, which internally processes data to reduce latency and consumption of bandwidth. Using facial recognition combined with other biometric data for increased security and user interaction, employing cloud-based solutions for scalable and strong face detection, and merging AI and machine learning with advanced neural networks for increased accuracy.

Future directions for real-time face detection in IoT include creating more sophisticated algorithms for improved accuracy even in challenging conditions, optimizing algorithms for low-power consumption critical for battery-powered devices, addressing data privacy and security concerns through techniques like federated learning and on-device processing, and incorporating face detection with augmented and virtual reality for immersive user experiences. Despite its potential, it has several kinds of difficulties, such as low computing power, inconsistent illumination and surroundings factors, concerns about data privacy, and scaling problems. To overcome these challenges, edge computing and lightweight models are utilized and optimized for IoT devices, integrating robust pre-processing techniques and adaptive algorithms, employing on-device processing and encryption to protect data, and using scalable cloud infrastructure with efficient data management practices. However, for Internet of Things applications, real-time face identification is a game-changing technology that provides better automation, personalized experiences, and increased security.

The topics of the issue include but not limited to the following:

Enhancing IoT Applications with Real-Time Facial Recognition.
Role of Real-Time Face Detection in Smart Homes.
Employing Real-Time Face Detection for Personalized Retail Experiences.
Challenges and Solutions in Real-Time Face Detection for IoT Devices.
Optimizing Real-Time Face Detection for Low-Power IoT Devices.
Ensuring Data Privacy and Security in Real-Time Face Detection for IoT Applications.
Advanced Algorithms for Accurate Real-Time Face Detection in IoT Systems.
Integrating Biometric Data for Enhanced Security and User Interaction in IoT.
Future of Real-Time Face Detection in IoT.
Utilization of Machine Learning Models for Real-Time Face Detection in IoT.

Guest Editor:

Dr.Rahmat Widia Sembiring

Department of Computer and Informatic,

Politeknik Negeri Medan,

North Sumatra 20155, Indonesia

Email id: [email protected], [email protected]

Official Page:

http://itec.pkb.edu.my/v4/rahmat_widia_sembiring.jsp

Scholar Page:

https://scholar.google.com/citations?user=hg_mkGwAAAAJ&hl=en

Dr.Jasni Mohammad Zain

Institute for Big Data Analytics and Artificial Intelligence (IBDAAI),