# Biometrics for Internet-of-Things Security: A Review
## 4 Biometric-Based Systems for IoT-Oriented Authentication
### Single-Modal Versus Multi-Modal Biometric Authentication Systems
#### 4.1.2 Multi-Modal Biometric Authentication Systems  
Multi-modal biometrics refers to the use of multiple sources of biometric information. Multi-modal biometric systems combine biometric evidence from multiple biometric traits (e.g., face and fingerprint) to improve recognition accuracy [63]. The obvious advantages of a multi-modal biometric system over its single-modal counterpart include better recognition accuracy and stronger security [64; 65]. Regarding recognition accuracy, multi-modal biometric systems collect and fuse data from more than one trait. The fused data are more discriminative, enabling multi-modal biometric systems to perform better than single-modal biometric systems [64]. In terms of security, multi-modal biometric systems are more robust [65]. If one modal fails for an unknown reason, then another modal can still work to obtain authentication. Moreover, the use of multi-modal biometric systems increases the difficulty for attackers to spoof multiple biometric traits of an individual [65].  
With more and more IoT devices equipped with high-resolution cameras, [PERSON] et al. [44] presumed that it is possible to capture iris and face images simultaneously with a multi-modal biometric system. In this paper, the biometric features extracted from the captured iris and face images with fiducial point localization and Gabor filtering are stored on the IoT devices as templates. In the authentication stage, the stored templates are used to identify and authenticate IoT device users. [PERSON] et al. [45] designed a biometric authentication system for information security and access control of IoT devices. The proposed system uses both hand geometry scan and gesture of the user for authentication purposes. Machine learning techniques are also applied to feature classification.  
[PERSON] et al. [46] proposed a face- and voice-based multi-modal biometric scheme for resource-limited IoT devices. The proposed scheme fuses feature data from face images and voice signals using discriminant correlation analysis (DCA) and classifies the features through the K-nearest neighbors (KNN) algorithm. Benefitted from the feature fusion, the multi-modal system increases recognition accuracy by more than 50% compared to that using only face or voice features. To strengthen network security, [PERSON] et al. [47] extracted a private key from multi-modal biometrics (fingerprint and finger-vein) to authenticate and validate blockchain transactions. Experiment results demonstrate that the proposed method attains a high security level in defending spoofing and signature forgery with high throughput and low latency. [PERSON] et al. [48] utilized ear and arm gesture features for user authentication in the application scenario where users answer phone calls. In this work, similarity scores are computed according to ear features using local phase quantization and arm gesture features based on four statistical metrics. Furthermore, a score-level fusion mechanism is used to calculate the final matching score such that the overall system performance is enhanced.  
In this sub-section, several multi-modal biometric systems used for IoT-oriented authentication are reviewed. It follows that combinations of biometric traits may vary, for example, iris and face, face and voice, fingerprint and finger-vein, or ear and arm gesture features. However, no one combination is absolutely better than the others and the selection of combinations is application dependent. With pros and cons, it is not always desirable to use multi-modal biometrics. It remains an open issue as to how to effectively combine/fuse multiple biometric features and it is unclear about the exact cost of applying more than one biometric trait. None of these topics are discussed in any of the above papers.