# Biometrics for Internet-of-Things Security: A Review
## 4 Biometric-Based Systems for IoT-Oriented Authentication
### Single-Modal Versus Multi-Modal Biometric Authentication Systems  
#### 4.1.1 Single-Modal Biometric Authentication Systems  
A single-modal biometric authentication system uses information from only one biometric trait (e.g., fingerprint or face) for user authentication to prevent unauthorized access to IoT devices and services. A biometric-based authentication system offers convenience and strong security compared to conventional password-based authentication. There is ongoing, intensive research in biometric authentication systems for the IoT [9]. The incorporation of biometrics in the IoT is also called the internet of biometric things (IoBT), first introduced by [PERSON] et al. [60]. Single-modal biometric authentication systems using different traits that appear in existing articles are reviewed below. _Fingerprint_: [PERSON] et al. [25] proposed an attendance system applying fingerprint-based biometric authentication on a portable IoT device. Moreover, the cloud is used to store the attendance records, making data easy to be accessed and retrieved. [PERSON] and [PERSON] [26] introduced how to build a low-cost biometric system using Raspberry Pi, which is similar to a credit-sized mini-computer. In this study, the Raspberry Pi is used as a remote node and the enrolled biometric information (e.g., fingerprint data) is encrypted by a cryptographic algorithm and stored in the cloud. [PERSON] and [PERSON] [27] enhanced home-IoT security using biometric authentication. In this paper, a fingerprint-based authentication system is implemented with Raspberry Pi together with several types of sensors. [PERSON] and [PERSON] [28] developed a cross-layer biometric recognition system that includes both software and hardware layers in a chaining structure. In this work, biometrics (e.g., fingerprint) is adopted. [PERSON] et al. [29] proposed a door lock system using fingerprints to unlock doors. The proposed system is composed of a fingerprint scanner, a magnetic solenoid lock, an Arduino board, and an LCD. This system is considered a good replacement of keys, locks, and cards and brings convenience and efficiency to organizations such as banks. [PERSON] et al. [31] presented a biometric scheme named BioSec to provide user authentication for edge devices in IoT and Industry 4.0. Specifically, fingerprint-based biometrics are utilized in BioSec, and fingerprint data is secured in both the transmission channel and the database using the standard symmetric encryption method. Figure 3: An example of a typical biometric authentication system (adapted from [22]). _Face:_ With more and more IoT devices embedded with camera sensors in numerous applications in different industries, [PERSON] et al. [32] proposed a framework for biometric-based end-to-end IoT authentication as a security solution and included face recognition as a case study. [PERSON] and [PERSON] [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. [PERSON] et al. [34] integrated biometric authentication into the green IoT to secure the personal assistants (e.g., Google Assistant, Alexa and Cortana). In this system, local binary pattern histograms as face features are extracted and integrated with machine learning to achieve user authentication in the green IoT scenario. [PERSON] et al. [35] proposed a decentralized IoT authentication framework based on face recognition in the background of lockdowns during COVID-19 outbreaks. The proposed edge computing framework includes three layers, namely physical layer, edge layer and face detection using the convolutional neural network (CNN). The proposed scheme is evaluated on several benchmark databases (e.g., WIDER FACE) and the performance in terms of recognition accuracy and latency is compared with state-of-the-art face recognition methods. _ECG_: [PERSON] et al.