The field of biometric authentication and physiological monitoring is moving towards more innovative and reliable methods. Researchers are exploring new approaches to address the limitations of traditional authentication methods, such as vulnerability to spoofing attacks and motion artifacts. One notable direction is the use of multimodal biometric features, including PPG signals, vibration signals, and bone-conducted sounds. These methods have shown promising results in terms of accuracy and resistance to attacks. Additionally, there is a growing interest in improving the efficiency and scalability of remote photoplethysmography (rPPG) techniques, which enable non-contact physiological monitoring. Noteworthy papers include MTL-RAPID, which proposes a multitask joint training strategy for PPG authentication, and FingerSlid, which uses vibration motors and accelerometers to sense biometric features of sliding fingers. Other notable papers include TeethPass+, which uses earbuds to collect occlusal sounds for authentication, and ME-rPPG, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time constraints in rPPG. These advances have the potential to enable more secure, convenient, and accurate biometric authentication and physiological monitoring systems.