Multimodal Biometric has been widely researched to improve social network user communication. Appropriate modeling of authentication and access control is highly required to perform precise system analysis in a social network framework. In this work, a Multimodal Biometric Secured (MBSec) Social Network User Communication framework to provide user authentic social communication in a safe environment is investigated. The multimodal user biometric features (i.e. face and fingerprint) with neighborhood feature pixel dominancy stored in a spatial vector are gene encoded to generate biometric identity keys. The authenticity of user is validated by decoding gene encoded biometric features with biometric identity keys. User relational coefficient is calculated for users, using prior knowledge instances shared between different social users. The social network user’s relational coefficient is sequenced in a matrix to identify the occurrence of authentic user relational social communication. Experiments were conducted using face and fingerprint images collected from BioSecure dataset. Our results demonstrate that the proposed social network user communication framework is able to significantly improve the true positive rate of authenticated users, compared to conventional FR approaches that only make use of a single model. Further, we demonstrate that our MBSec framework has a low social network authentication time and access control time.

中文翻译：

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用于社交网络通信的生物特征安全邻域向量关系系数框架

多模态生物特征已被广泛研究以改善社交网络用户沟通。在社交网络框架中执行精确的系统分析非常需要适当的身份验证和访问控制建模。在这项工作中，研究了一种多模式生物特征安全（MBSec）社交网络用户通信框架，以在安全的环境中提供用户真实的社交通信。具有存储在空间向量中的邻域特征像素优势的多模态用户生物特征（即面部和指纹）被基因编码以生成生物特征身份密钥。通过使用生物识别密钥解码基因编码的生物特征来验证用户的真实性。使用不同社交用户之间共享的先验知识实例为用户计算用户关系系数。社交网络用户的关系系数在矩阵中排序，以识别真实用户关系社交通信的发生。使用从 BioSecure 数据集收集的面部和指纹图像进行实验。我们的结果表明，与仅使用单个模型的传统 FR 方法相比，所提出的社交网络用户通信框架能够显着提高经过身份验证的用户的真实阳性率。此外，我们证明了我们的 MBSec 框架具有较低的社交网络身份验证时间和访问控制时间。我们的结果表明，与仅使用单个模型的传统 FR 方法相比，所提出的社交网络用户通信框架能够显着提高经过身份验证的用户的真实阳性率。此外，我们证明了我们的 MBSec 框架具有较低的社交网络身份验证时间和访问控制时间。我们的结果表明，与仅使用单个模型的传统 FR 方法相比，所提出的社交网络用户通信框架能够显着提高经过身份验证的用户的真实阳性率。此外，我们证明了我们的 MBSec 框架具有较低的社交网络身份验证时间和访问控制时间。