Modern technology may advance quickly, especially in the area of computer vision. Examples of this include facial recognition systems that can be integrated into smart home automation systems, access control systems, and security systems cards as well as security measures. The issue of unauthorized individuals using a legitimate authentication code to get access to ATMs is present when they are installed. This endeavor only provides support to the user in cases where the user is genuine or when the user’s validity has been verified by the real ATM card user. The users are verified by comparing their pictures taken in front of the ATM with the ones stored in the database. If the user is authentic, the fresh image is used to train the system for improved accuracy. In the case that an unauthorized user is found, a web connection to the registered cellphone number of the ATM card holder is offered. This is done to confirm that the unauthorized user has accessed the account; only then is the user deemed authentic. To identify the people utilizing the system, machine learning techniques and the histogram algorithm are employed. This system processes the input picture using OpenCV and utilizes the Haar cascade classifier to identify faces in the image, and local binary pattern is used to recognize faces.