Security is one of the essential requirements in restricted areas to protect valuable assets and sensitive information ensuring human safety. The traditional physical security system has some limitations to provide authentication to access official information in restricted areas. With the growing demand of modern society, the requirement of face detection has become an utmost important issue. However, a biometric technique is used for human recognition systems to understand human behavior for security and identification through fingerprints, facial features, gaits palms, and voice. While crafting a facial recognition system, the level of recognition accuracy mainly relies upon several factors such as facial expressions, facial positions, facial attributes, and the distance coverage by the camera. This research proposes a face recognition system using OpenCV, Haar cascade classifier, and local binary pattern (LBP) classifier where the optimized distance covered by the webcam is found to improve recognition efficiency. The performance metrics of both classifiers have been measured to detect the face in two cases. In the first case, the distance is not considered while applying the classifiers, and in the second case, the distance is considered to find the optimal accuracy. Our experimental study shows that the Haar cascade classifier provides better accuracy of 99.60% than the LBP classifier which provides 99.09% accuracy. Furthermore, our experimental results reveal that the Haar cascade classifier provides 99.82% accuracy when the maximum distance is 2.73 m and the average distance from webcam to face is 1.88 m. Similarly, the LBP classifier provides 99.56% accuracy when the maximum distance is 2 m, but the average distance is 1.41. So, this work favors the Haar cascade classifier over the LBP classifier concerning accuracy and average distance.