Biometric authentication is a widely used method for verifying individuals’ identities using
photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique
that measures the heart rate, which can vary from person to person. However, these signals can
also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the
system can accurately identify and authenticate the user despite these variations is a significant
challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D
image that visually represents the time-varying frequency content of multiple PPG signals from the
same human using the scalogram technique. Afterward, the features fusion approach is developed
by combining features from the hybrid convolution vision transformer (CVT) and convolutional
mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms
for the classification of human identity. This hybrid model has the potential to provide more accurate
and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP),
F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model’s
performance in accurately distinguishing genuine individuals. The results of extensive experiments
on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of
97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system.
These results suggest that the proposed method performs well in accurately classifying or identifying
patterns within the PPG signals to perform continuous human authentication.