Standard networking protocols can be very vulnerable to spoofing attacks when extra precautions are not taken by applications to verify the identity. There are several ways to spoof the facial recognition using photograph, video, and three-dimensional (3D) model of a valid user. Studies show that there have been significant improvements in detecting photograph, video, and 3D model spoofing individually, but no such improvement in detecting all types of spoofing in a single design. Recent studies also show that liveness monitoring using the facial features has potential to improve security, especially in biometric systems. In this work, an omproved method using acoustics and liveness monitoring system to better detect spoofing is introduced. The proposed system includes an ultrasonic range sensing module, a light dependent resistor (LDR) for detecting light intensity, and an eye blinking password is developed for liveness detection of the users. Eye blinking password is generated with the help of local facial features such as eye blinking and chin movement pattern using a real-time generic web-camera. The improper lighting conditions are corrected by placing an external light source to provide suitable lighting for the system. The proposed system is tested by conducting experiments using 20 valid users and 80 other users with variations in user's expressions. According to the experimental results, the proposed system achieves 100% face recognition accuracy and 100% liveness detection performance.