Face-based age estimation using improved Swin Transformer with attention-based convolution

Abstract

Recently Transformer models is new direction in the computer vision field, which is based on self multihead attention mechanism. Compared with the convolutional neural network, this Transformer uses the self-attention mechanism to capture global contextual information and extract more strong features by learning the association relationship between different features, which has achieved good results in many vision tasks. In face-based age estimation, some facial patches that contain rich age-specific information are critical in the age estimation task. The present study proposed an attention-based convolution (ABC) age estimation framework, called improved Swin Transformer with ABC, in which two separate regions were implemented, namely ABC and Swin Transformer. ABC extracted facial patches containing rich age-specific information using a shallow convolutional network and a multiheaded attention mechanism. Subsequently, the features obtained by ABC were spliced with the flattened image in the Swin Transformer, which were then input to the Swin Transformer to predict the age of the image. The ABC framework spliced the important regions that contained rich age-specific information into the original image, which could fully mobilize the long-dependency of the Swin Transformer, that is, extracting stronger features by learning the dependency relationship between different features. ABC also introduced loss of diversity to guide the training of self-attention mechanism, reducing overlap between patches so that the diverse and important patches were discovered. Through extensive experiments, this study showed that the proposed framework outperformed several state-of-the-art methods on age estimation benchmark datasets.

Transformative Approach for Heart Rate Prediction from Face Videos Using Local and Global Multi-Head Self-Attention

“…While the use of transformers in rPPG prediction is hardly explored, there are some relevant works in this area. A temporal difference transformer was introduced [43,44] with the quasi-periodic rPPG features to represent the local spatiotemporal features and evaluated the results on multiple public datasets. Another notable contribution, Efficientphys [45], introduced an end-to-end neural architecture for device-based physiological sensing and conducted a comprehensive comparison with convolutional neural networks.…”

“…This highlights the model's consistency. This study draws motivation and inspiration from the rPPGTr paper authored by [43,55].…”