A Survey on Multimodal Biometrics and the Protection of Their Templates <s> Multibiometric Systems <s> With the pace of increasing online transactions and communication, the demand for security and privacy increases. To protect confidential information and to authenticate people electronically, several solutions already introduced. Traditional biometric systems that are based on single biometric usually suffer from problems like impostorspsila attack or hacking, unacceptable error rates. To improve security and privacy and systempsilas reliability two or more biometrics of the same identity could be combined in a method that enhances the efficiency of the system. The biometric information, however, is irreplaceable information, when it is compromised. Thereby, one must give a special attention to protection of such information. We propose a novel protection technique for the biometric information, especially the feature information and the templates. The point of our proposal is securely embeds and extracts an iris template in a fingerprint image using a combined DWT and LSB based biometric watermarking algorithm in each authentication. The embedded data travel through insecure communication line like the Internet, and they are used in matching process. This technique causes security against eavesdropping and replay attacks on the Internet, because the watermark embedded transmitted data are used in the authentication session after watermark extraction. <s> BIB001 </s> A Survey on Multimodal Biometrics and the Protection of Their Templates <s> Multibiometric Systems <s> Fuzzy commitment schemes have been established as a reliable means of binding cryptographic keys to binary feature vectors extracted from diverse biometric modalities. In addition, attempts have been made to extend fuzzy commitment schemes to incorporate multiple biometric feature vectors. Within these schemes potential improvements through feature level fusion are commonly neglected. In this paper a feature level fusion technique for fuzzy commitment schemes is presented. The proposed reliability-balanced feature level fusion is designed to re-arrange and combine two binary biometric templates in a way that error correction capacities are exploited more effectively within a fuzzy commitment scheme yielding improvement with respect to key-retrieval rates. In experiments, which are carried out on iris-biometric data, reliability-balanced feature level fusion significantly outperforms conventional approaches to multi-biometric fuzzy commitment schemes confirming the soundness of the proposed technique. <s> BIB002 </s> A Survey on Multimodal Biometrics and the Protection of Their Templates <s> Multibiometric Systems <s> The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance. <s> BIB003
A multibiometric system increases the degree of confidence while the accuracy, throughput and scalability could be well estimated. Approximately, using the proposed fusion levels for different biometric traits in unconstrained environments and after the experimental performance analysis, there is an ability to reduce the levels of noise BIB003 BIB001 . On the other hand, multimodalities overcome limitations such as error-correcting capability and non-universality and this is a field which requires improvements  BIB002 .