User authentication for accurate biometric systems is becoming necessary in modern real-world applications. Authentication systems based on biometric identifiers such as faces and fingerprints are being applied in a variety of fields in preference over existing password input methods. Face imaging is the most widely used biometric identifier because the registration and authentication process is noncontact and concise. However, it is comparatively easy to acquire face images using SNS, etc., and there is a problem of forgery via photos and videos. To solve this problem, much research on face spoofing detection has been conducted. In this paper, we propose a method for face spoofing detection based on convolution neural networks using the color and texture information of face images. The color-texture information combined with luminance and color difference channels is analyzed using a local binary pattern descriptor. Color-texture information is analyzed using the Cb, S, and V bands in the color spaces. The CASIA-FASD dataset was used to verify the proposed scheme. The proposed scheme showed better performance than state-of-the-art methods developed in previous studies. Considering the AI FPGA board, the performance of existing methods was evaluated and compared with the method proposed herein. Based on these results, it was confirmed that the proposed method can be effectively implemented in edge environments.

In this paper, we propose a hospital sensor network deployment method for smart healthcare systems. Since sensor nodes in hospitals are always in an environment where power can be supplied, it is essential to have stable network connectivity by achieving optimal gateway deployment, rather than focusing on energy efficiency. The proposed technique leads to an access point (AP) layout that minimizes the overall network operation cost. The operation cost is calculated per unit time, and it includes installation cost and maintenance cost. In addition, group numbers are assigned to sensor nodes for guaranteeing network connectivity, no matter where the mobile sensor devices move. The performance of the proposed methodology has been verified through numerical experiments.