Abstract

Face recognition (FR) is one of the most widely used biometric methods for identity authentication. Although most of the recently proposed methods demonstrate remarkable performance on high-quality datasets, such as LFW, their effectiveness is limited when assessed on low-resolution images. To address this challenge, knowledge distillation and super-resolution techniques have been applied, primarily using the ResNet architecture. However, the performance of deep learning approaches depends in part on the architecture used. In this study, we use neuroevolution with a genetic algorithm (GA) to design Convolutional Neural Networks (CNNs) automatically for low-resolution (LR) FR. To reduce the search time, a binary classifier is used to identify which generated architectures should be trained and which should not. The selected architectures are then trained and evaluated using QUMUL-TinyFace (training partition), a native LR dataset, to obtain their fitness, while the remaining architectures are assessed using a performance predictor model to estimate their fitness, bypassing the training stage. The classifier and performance predictor are trained using the CNN architectures evaluated from previous generations, with the architecture encoding used as a feature vector. The proposed method was assessed on both the QMUL-TinyFace (for face identification) and QMUL-SurvFace (for face verification) datasets, achieving a rank-1 recognition rate of 74.7% and a mean verification accuracy rate of 85.1%, respectively, outperforming results from previously published methods.