Face recognition systems are increasingly deployed in security-sensitive applications, yet remain vulnerable to spoofing attacks using images, videos, or deepfake content. To address this threat, this study proposes a multi-method active liveness detection framework designed to enhance spoofing resistance in real-time facial authentication. To address this threat, this study proposes an advanced spoofing detection approach for facial recognition systems by integrating three active liveness detection methods: blink detection, head motion analysis, and facial expression recognition. Each technique is designed to counter spoofing attempts involving static images, videos, or deepfake technology. By incorporating active user interaction, the system prompts actions that are difficult to replicate using pre-recorded media. The proposed framework combines these methods into an anti-spoofing system capable of real-time operation with moderate computational demands. Experimental tests conducted under controlled conditions demonstrate consistent performance, achieving accuracy rates of up to 100% for blink detection and 90% for head movement analysis. However, findings highlight the need for improvement in emotion recognition and further testing in diverse environments to enhance system robustness.