Abstract
A face‐based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric‐based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud‐based environment. The deep learning approach extracted features from multicolor space to obtain more information from the input face image regarding luminance and chrominance data. These features are combined and selected by the Minimum Redundancy Maximum Relevance (mRMR) algorithm to provide an efficient and discriminate feature set. Finally, the extracted deep color‐based features of the face image are used for face spoofing detection in a cloud environment. The proposed method achieves stable results with less training data compared to conventional deep learning methods. This advantage of the proposed approach reduces the time of processing in the training phase and optimizes resource management in storing training data on the cloud. The proposed system was tested and evaluated based on two challenging public access face spoofing databases, namely, Replay‐Attack and ROSE‐Youtu. The experimental results based on these databases showed that the proposed method achieved satisfactory results compared to the state‐of‐the‐art methods based on an equal error rate (EER) of 0.2% and 3.8%, respectively, for the Replay‐Attack and ROSE‐Youtu databases.
Highlights
Nowadays, the Internet of Things (IoT) affects human lives in a wide range of technology from smart homes to smart cities
We presented a novel approach based on hybrid convolutional neural network (CNN) models on different color spaces for IoT-based cloud computing
The proposed multicolor deep featurebased approach outperformed the baseline methods on the Replay-Attack database, while achieving competitive results on the ROSE-Youtu database
Summary
The Internet of Things (IoT) affects human lives in a wide range of technology from smart homes to smart cities. An enormous number of IoT devices are utilized for collecting and analyzing information for different reasons, such as healthcare, security, and management. Biometric authentication can be utilized for identifying a person in wireless communication This authentication requires using personal attributes, such as speech, face, fingerprints, palmprint, gait, and iris [2]. This kind of authentication is based on a comparison between the physical aspect of the client that is collected with the help of different sensors and a copy that was stored. The physiological information of clients is more reliable when compared to knowledge-based or token-based methods because this information is unique and not shareable For this reason, IoT-based cloud computing systems for authentication of clients applied their biometric information
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