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SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition

机译：SPPCANET：3D面部识别的简单深度学习的特征提取方法

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摘要

A Sparse Principal Component Analysis Network (SpPCANet) based feature extraction is proposed here for 3D face recognition. The network consists of three basic components: (1) Multistage sparse principal component analysis filters, (2) Binary hashing, and (3) Block-wise histogram computation. Here, the sparse principal component analysis is used to learn multistage filter banks at the convolution stage, which is followed by binary hashing for indexing and block-wise histogram for pooling. Finally, a linear support vector machine (SVM) is used for classifying the features extracted by SpPCANet. The proposed network SpPCANet is a lightweight deep learning network. Three well-known 3D face databases, namely, Frav3D, Bosphorus3D, and Casia3D, are used for validating the proposed system. This proposed network has been extensively studied by varying different parameters, such as the number of filters at the convolution layer and the size of filters at the convolution layer and size of non-overlapping blocks at the pooling layer. Handling all types of variation of faces available in Frav3D, Bosphorus3D, and Casia3D databases, the system has acquired 96.93%, 98.54%, and 88.80% recognition rates, respectively.
机译：这里提出了一种稀疏的主成分分析网络（SPPCanet）的特征提取，用于3D面部识别。该网络由三个基本组件组成：（1）多级稀疏主成分分析过滤器，（2）二进制散列，（3）块 - 明智直方图计算。这里，稀疏主成分分析用于在卷积阶段学习多级滤波器组，然后是用于索引和块明智直方图的二进制散列。最后，线性支持向量机（SVM）用于对SPPCanet提取的功能进行分类。所提出的网络SPPCanet是一种轻量级的深度学习网络。三个众所周知的3D面部数据库，即FRAV3D，Bosphorus3D和CASIA3D用于验证所提出的系统。通过改变不同的参数已经广泛地研究了该网络，例如卷积层处的滤波器的数量和卷积层的卷积层的尺寸和汇集层的非重叠块的大小的滤波器的尺寸。处理FRAV3D，Bosphorus3D和Casia3D数据库中可用的所有类型的面部变化，系统分别获得了96.93％，98.54％和88.80％的识别率。

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