Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research on face recognition, small-scale face recognition has achieved good recognition results, but in the era of big data, the existing small-scale face recognition methods have gradually failed to meet the social needs, and how to get a good face recognition effect in the era of big data has become a new research hotspot. Based on this, this paper aims to optimize the existing face recognition algorithm, study the face recognition method driven by big data, and propose a deep learning multi feature fusion face recognition algorithm driven by big data. First, for the problem that 2DPCA (Two-dimensional Principle Component Analysis) can well extract the global features of the face under large samples, but the local features of the face are difficult to process, this paper uses the LBP (Local Binary Pattern, LBP) algorithm to extract the texture features of the face, and the extracted texture features are integrated with the global features extracted by 2DPCA to multi-feature fusion, so that the fused features can take into account both global and local features, and have better recognition results. Then using the obtained fusion features as input, training in a convolutional neural network, and measuring the similarity based on the feature vectors of the sample set and the training set after the training, can realize multi-feature fusion face recognition. Through the analysis of simulation experiments, it is found that, compared with the use of global features or local features alone, the fusion features obtained by multi-feature fusion of global features extracted by 2DPCA and local features extracted by LBP algorithm have better recognition effect in the big data environment. After convolutional neural network trains and recognizes this feature, a high recognition accuracy rate is obtained, which can show that the face recognition method designed in this paper has good application potential in the era of big data. In the background of big data, the accuracy of face recognition can reach more than 90%, which can meet the needs of society well.

如今，随着科学技术的飞速发展，提出了大数据时代，并引发了各行各业的改革。人脸识别是一种非接触，非强制，友好，和谐的生物识别方法，在国家安全和社会保障领域具有良好的应用前景。随着人脸识别研究的不断深入，小规模人脸识别取得了良好的识别效果，但在大数据时代，现有的小规模人脸识别方法已逐渐无法满足社会需求，以及如何获得。大数据时代良好的人脸识别效果已经成为新的研究热点。在此基础上，本文旨在优化现有的人脸识别算法，研究大数据驱动的人脸识别方法，提出了一种基于大数据的深度学习多特征融合人脸识别算法。首先，针对2DPCA（二维主成分分析）可以很好地提取大样本下人脸的全局特征，但人脸的局部特征难以处理的问题，本文使用LBP（局部二值模式） LBP）算法提取人脸的纹理特征，并将提取的纹理特征与2DPCA提取的全局特征集成在一起进行多特征融合，使融合特征既可以考虑全局特征又可以兼顾局部特征，具有更好的融合效果识别结果。然后使用获得的融合特征作为输入，在卷积神经网络中进行训练，通过训练后的样本集和训练集的特征向量对相似度进行测量，可以实现多特征融合人脸识别。通过仿真实验分析，发现与单独使用全局特征或局部特征相比，通过2DPCA提取的全局特征和LBP算法提取的局部特征的多特征融合获得的融合特征具有更好的识别效果。在大数据环境中。通过卷积神经网络训练并识别出该特征后，获得了较高的识别准确率，这表明本文设计的人脸识别方法在大数据时代具有良好的应用潜力。在大数据的背景下，人脸识别的准确性可以达到90％以上，