This paper proposes a mathematical model for decomposing a range face image into four basic components (named ‘complement components’) in conjunction with a simple approach for data-level fusion to generate thirty-six additional hybrid components. These forty component faces composing a new face image space called the ‘complement component face space.’ The main challenge of this work was to extract relevant features from the vast face space. Features are extracted from the four basic components and four selected hybrid components using singular value decomposition. To introduce diversity, the extracted feature vectors are fused by applying the crossover operation of the genetic algorithm using a Hamming distance-based fitness measure. Particle swarm optimization-based feature selection is employed on the fused features to discard redundant feature values and to maximize the face recognition performance. The recognition performances of the proposed feature set with a support vector machine-based classifier on three accessible and well-known 3D face databases, namely, Frav3D, Bosphorus, and Texas3D, show significant improvements over those achieved by state-of-the-art methods. This work also studies the feasibility of utilizing the component images in the complement component face space for data augmentation in convolutional neural network (CNN)-based frameworks.

本文提出了一种数学模型，用于将范围人脸图像分解为四个基本成分（称为“补足成分”），并采用一种简单的数据级融合方法来生成三十六个其他混合成分。这四十个组成部分的面孔组成了一个新的面孔图像空间，称为“补充组成部分面孔空间”。这项工作的主要挑战是从广阔的面部空间中提取相关特征。使用奇异值分解从四个基本组件和四个选定的混合组件中提取特征。为了引入多样性，使用基于汉明距离的适应性度量通过应用遗传算法的交叉运算来融合提取的特征向量。对融合后的特征采用基于粒子群优化的特征选择，以丢弃多余的特征值并最大化人脸识别性能。建议的功能集与基于支持向量机的分类器在三个可访问且众所周知的3D人脸数据库（即Frav3D，Bosphorus和Texas3D）上的识别性能显示出与现有技术相比所取得的显着改进方法。这项工作还研究了在基于卷积神经网络（CNN）的框架中利用补充分量脸部空间中的分量图像进行数据增强的可行性。Frav3D，Bosphorus和Texas3D显示出比通过最新技术所实现的显着改进。这项工作还研究了在基于卷积神经网络（CNN）的框架中利用补充分量脸部空间中的分量图像进行数据增强的可行性。Frav3D，Bosphorus和Texas3D显示出比通过最新技术所实现的显着改进。这项工作还研究了在基于卷积神经网络（CNN）的框架中利用补充分量脸部空间中的分量图像进行数据增强的可行性。