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.
Highlights
The Internet of Things (IoT) has become an important research domain, and their applications have shown their potential in recent years
We introduce the basics of the classical Sparse-Representation-based Classification (SRC) method, which is the foundation of the proposed framework, where the entire training samples consists of dictionaries that come from query facial images
EXPERIMENTAL RESULTS Considering the face occlusion detection framework constructed in our paper for arbitrarily occluded heads under complex background, we tested a large quantity of video sequences through using a common and stationary DVR, which simulate the monitoring system of banks
Summary
The Internet of Things (IoT) has become an important research domain, and their applications have shown their potential in recent years. In order to be successful in the commercial world, deep learning technology has been broadly used in action recognition, image recognition and computer vision fields [1], [2]. Existing IoT devices, such as ATM machines, are usually used to do some financial operations all over the countries. One important reason for this is its capacity in security application, which requires a video surveillance system to distinguish potentially dangerous users from normal users solely based on the users’ face [3]–[5]. Detecting potential criminals who have their faces covered is one of the applications of our paper, and this will remind ATM users not to cover his faces with something.
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