OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network

Abstract

Face recognition is considered as important research in computer vision applications, and it is regarded as the basic biometric security system. Research related to face recognition has been done in the past several years. Still, many more challenges associated with this field need to be addressed. Some literary works have designed the face recognition model on the relatively controlled environments; yet, their performance in general settings has been substandard This paper develops an Optimal Face Recognition Network (OPFaceNet) to recognize the face images affected by high noise and occlusion. The feature patterns subjected to noise like LBP, FLBP, and NRLBP are extracted. The average of all three patterns is given to the proposed Convolutional Neural Network (CNN) classifier. As the main contribution, the CNN model is enhanced by optimizing the Fitness Sorted Rider Optimization Algorithm (FS-ROA). This algorithm optimizes the hyperparameters of CNN like Convolutional layer, Pooling Layer, Fully connected layer, number of Hidden layers, and Type of Pooling. Finally, the simulation results show that the system achieves a good recognition rate of 97.2% and is robust against variations in terms of occlusion and noise when benchmarked over diverse datasets.