Age-Defying Face Recognition with Transformer-Enhanced Loss

TL;DR

This paper introduces a transformer-enhanced loss function for face recognition that improves age-invariance, achieving state-of-the-art results on datasets with aging variations by integrating transformer networks with metric loss.

Contribution

It proposes a novel transformer-based loss function for face recognition that enhances age-invariant feature learning, expanding the use of transformers in machine vision.

Findings

Achieves state-of-the-art results on LFW, CA-LFW, and AgeDB datasets.

Transformer-enhanced loss improves age-invariance in face recognition.

Combining transformer loss with metric loss enhances discriminative power.

Abstract

Aging presents a significant challenge in face recognition, as changes in skin texture and tone can alter facial features over time, making it particularly difficult to compare images of the same individual taken years apart, such as in long-term identification scenarios. Transformer networks have the strength to preserve sequential spatial relationships caused by aging effect. This paper presents a technique for loss evaluation that uses a transformer network as an additive loss in the face recognition domain. The standard metric loss function typically takes the final embedding of the main CNN backbone as its input. Here, we employ a transformer-metric loss, a combined approach that integrates both transformer-loss and metric-loss. This research intends to analyze the transformer behavior on the convolution output when the CNN outcome is arranged in a sequential vector. These sequential vectors have the potential to overcome the texture or regional structure referred to as wrinkles or sagging skin affected by aging. The transformer encoder takes input from the contextual vectors obtained from the final convolution layer of the network. The learned features can be more age-invariant, complementing the discriminative power of the standard metric loss embedding. With this technique, we use transformer loss with various base metric-loss functions to evaluate the effect of the combined loss functions. We observe that such a configuration allows the network to achieve SoTA results in LFW and age-variant datasets (CA-LFW and AgeDB). This research expands the role of transformers in the machine vision domain and opens new possibilities for exploring transformers as a loss function.