Boosting Based Implementationof Biometric Authentication in IoT

Abstract

In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.

Biometrics for Internet-of-Things Security: A Review

“…Face: With more and more IoT devices embedded with camera sensors in numerous applications in different industries, Hossain et al [32] proposed a framework for biometricbased end-to-end IoT authentication as a security solution and included face recognition as a case study. Thilagavathi and Suthendran [33] conducted automatic real-time face recognition from videos using existing algorithms such as Adaboost and local binary pattern histograms. The Haar features extracted from the face images are used for face authentication.…”

Deep Learning Based Image Recognition for 5G Smart IoT Applications

“…In [5], the author proposes a multi-mode deep learning (MMDL) method that integrates heterogeneous visual features by treating each type of visual feature as a way to optimize image recognition in the Internet of Things. In [6], the author implemented a real-time face recognition system in real-time through a high-level description language (such as python), and provided an innovative solution for automatic real-time face recognition for video. In [7], the author proposed a system that demonstrates that the Internet of Things can be applied in healthcare and daily life.…”