Face recognition is diversely used in modern biometric and security applications. Most of the current face recognition techniques show good results in a constrained environment. However, these techniques face many problems in real-world scenarios such as low-quality images, temporal variations and facial disguises creating variations in facial features. The reason for these deteriorating results is the employment of handcrafted features having weak generalization capabilities and neglecting the complexities associated with domain adaption in case of deep learning models. In this paper, we have studied the efficacy of deep learning methods incorporating simple noise-based data augmentation for disguise invariant face recognition (DIFR). The proposed method detects face in an image using Viola Jones face detector and classifies it using a pre-trained Convolutional Neural Network (CNN) fine-tuned for DIFR. During transfer learning, a pre-trained CNN learns generalized disguise-invariant features from facial images of several subjects to correctly identify them under varying facial disguises. We have compared four different pre-trained 2D CNNs, each with different number of learning parameters, based on their classification accuracy and execution time for selecting a suitable model for DIFR. Comprehensive experiments and comparative analysis have been conducted on six challenging facial disguise datasets. Resnet-18 gives the best trade-off between accuracy and efficiency, by achieving an average accuracy of 98.19% with an average execution time of 0.32 seconds. The promising results achieved in these experiments reflect the efficiency of the proposed method and outperforms the existing methods in all aspects.

人脸识别在现代生物识别和安全应用中被广泛使用。当前大多数人脸识别技术在受限的环境中显示出良好的效果。但是，这些技术在现实世界中面临许多问题，例如低质量的图像，时间变化和面部伪装会造成面部特征的变化。这些结果恶化的原因是使用了具有较弱泛化能力的手工功能，而在深度学习模型的情况下却忽略了与领域适应相关的复杂性。在本文中，我们研究了结合简单的基于噪声的数据增强的深度学习方法对伪装不变人脸识别（DIFR）的功效。提出的方法使用Viola Jones脸部检测器检测图像中的脸部，并使用针对DIFR进行了微调的预训练卷积神经网络（CNN）对其进行分类。在转移学习期间，经过预训练的CNN会从多个对象的面部图像中学习广义的伪装不变特征，以便在各种面部伪装下正确识别它们。我们根据分类精度和执行时间为DIFR选择合适的模型，比较了四个不同的预训练2D CNN，每个CNN具有不同数量的学习参数。对六个具有挑战性的面部伪装数据集进行了综合实验和比较分析。Resnet-18通过达到98.19％的平均精度和0.32秒的平均执行时间，可以在精度和效率之间取得最佳平衡。