Face Occlusion Recognition With Deep Learning in Security Framework for the IoT

Abstract

Currently, the security of the Internet of Things (IoT) has aroused great concern. Face detection under arbitrary occlusion has become a key problem affecting social security. This paper designs a novel face occlusion recognition framework in the security scene of IOT, which is used to detect some crime behaviors. Our designed framework utilizes the gradient and shape cues in a deep learning model, and it has been demonstrated to be robust for its superiority to detect faces with severe occlusion. Our contributions contain three main aspects: Firstly, we present a new algorithm based on energy function for face detection; Secondly, we use the CNN models to create deep features of occluded face; Finally, to check whether the detected face is occluded, novel sparse classification model with deep learning scheme is constructed. Statistical results demonstrate that, compared with the state of the arts, our algorithm is superior in both accuracy and robustness. Our designed head detection algorithm can achieve 98.89% accuracy rate even though there are various types of severe occlusions in faces, and our designed occlusion verification scheme can achieve 97.25% accuracy rate, at a speed of 10 frames per second.

Improvement of Identity Recognition with Occlusion Detection-Based Feature Selection

“…Instead of simply learning a network that categorizes the training dataset well, it creates a virtual dataset to learn the external environment or combines multiple networks into an ensemble to improve performance and design a multi-task network to make various attempts in which information complementarily aids learning. In addition to improving overall recognition performance, research on robust recognizers continues even when some information is lost due to occlusion [35][36][37][38][39]. These studies are developing an identity identification method that corresponds to the case where there is a loss of information due to obscuration, etc., rather than cases where the face is seen completely.…”

A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak

“…They claimed that complex dependencies of service modules aimed at optimizing the execution time and energy consumption during real-time execution of services. Authors of [22], have designed a face occlusion technique using an IoT device particularly focused on criminal behavior. Furthermore, it uses deep learning techniques and utilizes gradient and shape cues to yield the best results in face detection using face occlusion.…”

“…Authors of [22] , have designed a face occlusion technique using an IoT device particularly focused on criminal behavior. Furthermore, it uses deep learning techniques and utilizes gradient and shape cues to yield the best results in face detection using face occlusion.…”

Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things

“…Artificial Intelligence (AI) is rapidly spreading into Internet of Things (IoT) devices, including face recognition for smart security systems [1][2][3], voice assistant with AI speakers [4][5][6], and smart cars [7,8]. IoT edge devices, however, do not have sufficient resources to perform inference of complex deep neural networks (DNN) in a timely manner.…”