Lately, human recognition and identification has acquired much more attention than it had before, due to the fact that computer science nowadays is offering lots of alternatives to solve this problem, aiming to achieve the best security levels. One way is to fuse different modalities as face, voice, fingerprint and other biometric identifiers. The topics of computer vision and machine learning have recently become the state-of-the-art techniques when it comes to solving problems that involve huge amounts of data. One emerging concept is Artificial Neural networks. In this work, we have used both human face and voice to design a Multibiometric recognition system, the fusion is done at the feature level with three different schemes namely, concatenation of pre-normalized features, merging normalized features and multiplication of features extracted from faces and voices. The classification is performed by the means of an Artificial Neural Network. The system performances are to be assessed and compared with the K-nearest-neighbor classifier as well as recent studies done on the subject. An analysis of the results is carried out on the basis Recognition Rates and Equal Error Rates. Index Terms: Bioinformatiscs, Face, voice, Multibiometric recognition system, fusion at feature level, Artificial Neural Network(ANN).

中文翻译：

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使用人工神经网络进行个人识别的面部和语音生物识别系统的特征级融合

最近，人类识别和识别比以前获得了更多的关注，因为如今的计算机科学提供了许多替代方案来解决这个问题，旨在实现最佳的安全级别。一种方法是融合不同的模态，如面部、语音、指纹和其他生物识别标识符。在解决涉及大量数据的问题时，计算机视觉和机器学习的主题最近已成为最先进的技术。一个新兴概念是人工神经网络。在这项工作中，我们使用人脸和语音设计了一个多生物识别系统，融合是在特征级别通过三种不同的方案完成的，即预归一化特征的串联，合并归一化特征和从面部和声音中提取的特征的乘法。分类是通过人工神经网络进行的。系统性能将被评估并与 K 近邻分类器以及最近对该主题进行的研究进行比较。结果的分析是基于识别率和相等的错误率进行的。索引词：生物信息学、人脸、语音、多生物识别系统、特征级融合、人工神经网络（ANN）。结果的分析是基于识别率和相等的错误率进行的。索引词：生物信息学、人脸、语音、多生物识别系统、特征级融合、人工神经网络（ANN）。结果的分析是基于识别率和相等的错误率进行的。索引词：生物信息学、人脸、语音、多生物识别系统、特征级融合、人工神经网络（ANN）。