—The number of ATMs in various countries is increasing steadily and rapidly with the number of users increasing very widely. On the other hand, banks have become more interested in finding the best procedures to combat ATM crimes to ensure the safety and security of their customers and other cardholders. This has become an excellent target for some criminals or fraudsters, despite the limited amounts that can be withdrawn from these devices, given a maximum daily limit. We aim at implementing this system inside bank ATMs in order to detect objects like guns, hammers, and knives. Once the suspicious objects and actions are detected, we perform facial recognition to identify whether the suspect is a repeating offender. We use object, face, and action recognition algorithms to achieve our objective. Results showed that using our proposed algorithm is efficient in detecting threatening objects