Face-liveness detection is a pivotal component in modern biometric security systems, aimed at distinguishing between genuine human faces and spoofing attempts using static images, videos, or 3D models. The most recent methods and strategies for detecting face liveliness are outlined in this paper, with an emphasis on advancements in the software and hardware domains. Various approaches including texture analysis, motion-based techniques, and deep learning (DL) methods that leverage convolutional neural networks (CNNs) and other machine learning (ML) algorithms to enhance detection accuracy are explored. Additionally, the paper discusses the flaws and limitations of recent advances in technologies, including the trade-offs between detection performance and computational efficiency. Future directions for research are also proposed, focusing on the integration of multimodal liveness detection systems and the adoption of emerging technologies like augmented reality and blockchain for improved security.