Face recognition has been active research in the security domain. Human face recognition gains importance for developing a secured environment for the organization and also enhances the usage of artificial intelligence for security. Face recognition has been studied over the years for accurate recognition of complete face images. However, in the real case, the presence of occlusion and noise in the image significantly affects the performance of the recognition. Even though a lot of research has been carried out in handling the occluded and noisy image, more refinement is required to achieve high accuracy. This paper proposes a simple and efficient face recognition system with occlusion and noisy faces using the deep learning concept, as it has the advantage of handling all of it. The developed model undergoes four main steps like (a) preprocessing, (b) cascaded feature extraction, (c) optimal feature selection, and (d) recognition. Initially, the preprocessing of the face image is focused in terms of face detection by Viola-Jones algorithm. Further, a set of features termed as Local Diagonal Extrema Number Pattern (LDENP), Gradient-based directional features, and Gradient-based wavelet features are extracted for the cascaded feature extraction. As the collection of features is in a cascaded manner, it leads to providing irrelevant information of features. Hence, there is a need for optimal feature selection. The hybrid meta-heuristic concept, namely Multi-Verse with Colliding Bodies Optimization (MV-CBO), is developed with the integration of Colliding Bodies Optimization (CBO) and Multi-Verse Optimizer (MVO), and it is used for performing the optimal feature selection. Further, the optimally selected features are subjected to the optimized Deep Neural Network (DNN) for recognizing the faces, in which the proposed MV-CBO is used for optimizing the activation functions (sigmoid, tanh, Relu, ArcTan, and RRelu). The experimental findings on diverse datasets with occlusion and noises prove that the extensive experiments on several benchmark databases prove the ability of the proposed model over the existing face recognition approaches.

人脸识别一直是安全领域的积极研究。人脸识别对于为组织开发安全环境至关重要，还可以增强人工智能的安全性。多年来，已经对人脸识别进行了研究，以准确识别完整的人脸图像。但是，在实际情况下，图像中存在遮挡和噪点会显着影响识别性能。即使在处理遮挡和嘈杂的图像方面已进行了大量研究，但仍需要进行更多的改进才能获得较高的精度。本文提出了一种使用深度学习概念的具有遮挡和嘈杂脸部的简单高效脸部识别系统，因为它具有处理所有这些内容的优势。开发的模型要经历四个主要步骤，例如（a）预处理，（b）级联特征提取，（c）最佳特征选择和（d）识别。最初，通过Viola-Jones算法在人脸检测方面关注人脸图像的预处理。此外，提取称为局部对角极值数字模式（LDENP），基于梯度的方向性特征和基于梯度的小波特征的一组特征，以进行级联特征提取。由于特征的收集是级联的，因此导致提供不相关的特征信息。因此，需要最佳的特征选择。混合元启发式概念，即具有碰撞体优化的多版本（MV-CBO），是通过将碰撞体优化（CBO）和多版本优化器（MVO）集成在一起而开发的，用于执行最优功能选择。更多，最佳选择的特征将经过优化的深度神经网络（DNN）进行识别，其中建议的MV-CBO用于优化激活函数（S型，tanh，Relu，ArcTan和RRelu）。在具有遮挡和噪声的各种数据集上的实验结果证明，在几个基准数据库上的广泛实验证明了所提出的模型在现有人脸识别方法上的能力。