Driver drowsiness can negatively affect a person’s ability to stay alert, compromising not only their own safety but also the safety of others. In this work, we propose a comparison between a binary CNN+BiLSTM model and a BiLSTM model to predict whether an individual is alert or not. Using the UTA-RLLD dataset, which contains videos of individuals actually experiencing drowsiness, we process the positions of the eyes and mouth, as well as the distance between the chin and the nose, transforming these features into vectors that allow the model to capture the spatial information of each frame. A Bidirectional Long Short-Term Memory (BiLSTM) network is employed to capture the temporal dynamics across frames, including gradual changes in eye closure, yawning, and head movements. The experimental results show that the CNN+BiLSTM model achieves higher accuracy on the test dataset (77.59%) compared to the model using only BiLSTM layers (70.69%), demonstrating the advantage of integrating convolutional layers with BiLSTM.