Face recognition has been one of the most widely used sub-disciplines of machine learning for so many years. Face detection has been employed as an effective method in a wide range of applications such as surveillance systems and Forensic pathology in the area of machine vision. However, the accuracy of face detection has dramatically declined over the past decade due to wide-ranging challenges such as face detection with changes in face angle, the density of the crowds in an image, quality of light, etc which require special attention of researchers in response to these challenges. In the present study, a new sustainable approach to light changes for face detection based on local features is employed. In this method, the local binary pattern is extracted from face images and Principal Component Analysis is utilized to reduce the feature vectors’ dimension by the descriptor. Eventually, the features are classified using Ada Boost. Tests done on the images on the web show that face recognition accuracy is 100% in the low density crowd, 96% in the high-density crowd and proper light conditions, and 90% in the high-density crowd and poor light conditions.