Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> At present, the research of biometric recognition is gave more and more attentions. The iris recognition is a kind of biometrics technologies. Based on Daugman' and Wilds' iris localization algorithms, the paper is to present an improved iris localization method named two-phase approximation localization for authentication system. Firstly, we evaluate the image quality and abandon blurred pictures. And we estimate the center and radius of iris boundaries. After that, the method calculates the center and radius for the second time using vote method. The average time of localization reaches 2.0 seconds and 99.70% at accuracy. The method is much faster than Daugman's and Wilds' classical methods. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> Illumination variation is one of the most important challenges for robust face recognition system under real environment. It attracts more and more attention in face recognition field. In this paper, an improved retinal modeling is proposed to alleviate the adverse effect of lighting variation on face recognition. There are two main contributions. One is that it develops a new scheme to calculate appropriate adaptation factor through maximum filtering and illumination classification. The factor is quite crucial for illumination normalization by modeling the retinal information processing mechanism. The other is that an adaptive truncation based on the median statistics is used for contour enhancement. The proposed method can preserve image details, while achieves good illumination normalization results. Experimental results on the Extended Yale B face databases show that the new method achieves high recognition rates, and is quite effective in varying lighting condition, especially in difficult lighting situation. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> Imagine a criminal using your nanny cam to watch your house or to scream at your child-or even to post footage of your home on a crime boss's website. And suppose your refrigerator were spewing spam e-mail, enraging people you'd never even met. <s> BIB003 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using Kullback-Leibler divergence. In order to obtain the final decision on recognition, the multi decision on various color channels has been combined by employing mean rule. The decisions of H, S, Y, Cb and Cr color channels have been combined. The proposed technique overcome the conventional principle component analysis technique and achieved a recognition rate of 100% using the UPOL database. The major advantage is the fact that it is computationally less complex than the Daugman's algorithm and it is suitable for using visible light camera as opposed to the one proposed by Daugman where NIR cameras are used for obtaining the irises. <s> BIB004 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> In this competitive era, biometric systems provide more reliable security than traditional methods like passwords etc. Biometric systems perform person's authentication based on his physical traits. A number of biometric systems has been developed in the last few years such as fingerprints, hand and palm geometry, retina etc. Due to stability, uniqueness and non-replicable nature of vascular pattern, retinal recognition is the most stable biometric system. Retinal recognition performs person's identification based on the unique vasculature of retina. Generally, it is a three-step process, which includes pre-processing, segmentation and matching. Segmentation is the fundamental step, which becomes crucial in the presence of different pathological signs like exudates, lesions. If they are not removed in segmentation, then they produce false positives, hence leads to misclassification. To address this problem, this paper presents an efficient segmentation algorithm which aims to remove pathological effects from the diseased retinal images and improve matching results by reducing false recognition rate. Experimental results demonstrate the efficiency of proposed system. <s> BIB005 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> Speaker identification systems are becoming more important in today's world. This is especially true as devices rely on the user to speak commands. In this article, an analysis of how a text-independent voice identification system can be built is presented. Extracting the Mel-Frequency Cepstral Coefficients is evaluated and a support vector machine is trained and tested on two different data sets, one from LibriSpeech and one from in-house recorded audio files. The results show the ability for such systems to be utilized in both speaker identification and speaker verification tasks. <s> BIB006 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> The consumer Internet of Things (IoT) platforms are gaining high popularity. However, due to the open nature of wireless communications, smart home platforms are facing many new challenges, especially in the aspect of security and privacy. In this article, we first introduce the architecture of current popular smart home platforms and elaborate the functions of each component. Then we discuss the security and privacy challenges arising from these platforms and review the state of the art of the proposed countermeasures. We give a comprehensive survey on several new attacks on the voice interface of smart home platforms, which aim to gain unauthorized access and execute over-privileged behaviors to compromise the user's privacy. To thwart these attacks, we propose a novel voice liveness detection system, which analyzes the wireless signals generated by IoT devices and the received voice samples to perform user authentication. We implement a real-world testbed on Samsung's SmartThings platform to evaluate the performance of the proposed system, and demonstrate its effectiveness. <s> BIB007 </s> Multimedia Internet of Things: A Comprehensive Survey <s> b: VOICE BIO-METRICS <s> In the last decade, biometric has become one of the most common uses for verification and authentication of someone's personal identity. Biometrics are defined as the measurement and analysis of unique physical or behavioral characteristics. Fingerprint is undoubtedly the most well-known biometric, but in the recent years ocular recognition has taken a huge jump forward. Ocular recognition (OR) is one of the most reliable personal identification methods in biometrics that can have flexible applications as opposed to the iris recognition. The basic breakdown of the eye is the iris, cornea, retina, pupil, sclera, and conjunctiva. The iris is where eye is actually colored; this is determined by the amount and type of pigment in one's iris. Iris recognition is an accurate form of ocular recognition. It works by using biometric systems to apply pattern recognition to a person's eye since no two people have the same patterns in their eye. Ocular recognition has been used as an identification method on both Apple and Android devices without much popularity. The reason for this is mainly related to the issues with the underlying algorithm used that affects the accuracy and security measures. Many different algorithms have been proposed and tested; some are more accurate than others. Our goal is to investigate different technologies currently used to help combine the best parts of both (Iris and Retina) in order to propose a better system for future implementation. With the proper design, ocular recognition could be a great tool for user authentication in emerging Internet of Things (IoT) and mobile markets. <s> BIB008
With the growing deployment of smart homes and smart banking applications, i.e., voice to control everything, and authentication by voice recognition, provide attackers with an easy loophole which is one of the vital security challenges ahead. Voice biometrics are proposed by several researchers stretching up to the verification level of the security system. Voiceprint verification system based on DL exploiting SVM-Neural Network (SVM-NN) and Mel-Frequency Cepstral Coefficients (MFCC), and feature extraction technique to increase the security for M-IoT systems is proposed in BIB006 . In this work, the LibriSpeech dataset is utilized for training. Meng et al. BIB007 present a novel system based on a voice liveness detection system that utilizes wireless signals from IoT devices and received vocal samples for verification of Voice Control User Interfaces (VUI) decreasing spoofing attacks. The authors named their technique as WiVo. The feasibility and effectiveness of the proposed system are evaluated on the Samsung SmartThings testbed. The article also listed several attacks on VUI. A comprehensive survey on biometric-based IoT security issues, challenges, and techniques are presented in BIB003 ,  . Pjatkin et al. BIB004 proposed a probabilistic iris recognition approach using the UPOL database to overcome the PCA technique. HSI and YC b C r color spaces are used to generate the Probability Density Function (PDF). The KLD tool is used to extract colored eye iris information. Ocular Recognition (OR) for a secure IoT application has been proposed in the literature. Different OR techniques have been investigated in BIB008 . An algorithm based on the fusion of iris and retinal scanning is proposed for user authentication in Apple and Andriod devices. Retina based face recognition authentication system is presented in BIB002 . In this article, retina modeling is improved by accurate truncation adaption, illumination classification, and lighting estimation. Yale B database is utilized to validate their model. Ocular maladies damage the vascular patterns and create abnormalities in the retina, which hinders the accurate recognition for authentication. Researchers in BIB005 , proposed an efficient vascular recognition technique to overcome the effects caused by lesions and to extract region-based features from retina images. Gabor Wavelet is utilized for noise reduction and pre-processing, followed by segmentation. Blood vessel validation is performed to eliminate false detection by a 5-D feature extraction algorithm and classification based on SVM. The database is formed by 5-D extracted features. Finally, authentication is performed with the created database. Zhang et al. BIB001 proposed an improved Iris localization authentication method. The authors modeled the system by first evaluating the quality of the image taken according to the image intensity, clarity, and integrity. The clarity of the image is evaluated by using a block-variance method, and integrity is defined on the location of the pupil, i.e., the pupil must be in the center. The algorithm works by estimating the range of center, radius of the pupil, and pupil region extraction, thus reducing the delay in iris recognition.