A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups

This work presents a study on the effects of aging on the performance and reliability of facial authentication methods. First, a brief review of the literature on the effect of age on face recognition algorithms is presented, followed by a detailed description of the face aging datasets. In contrast with some recent studies, we demonstrate significant variations in authentication robustness between age groups. The second part of this paper focuses on a comprehensive comparative assessment on the effects across age groups. Four different face recognition algorithms are studied of which three are state-of-the-art neural network based models and the fourth one is a conventional machine learning model. Two different age range threshold settings (±3 in Experiment Category A and ±5 in Experiment Category B) of the age groups are adopted in the experimental analysis to get a proper comparison. Moreover, a synthetic aging method has been incorporated to augment the age data. Experimental result shows that the older adults groups are easier to identify with higher levels of accuracy and robustness compared to other age groups, while younger adults are the most challenging and false authentications are more likely to occur.