Multimodal-based Biometric authentication systems are more secure and protected because of unique biometric features of each person, and adding more than one biometric method it is more promising. Reliably resist against privacy threat, the goal of this system is to provide an easier and secure authentication system using face and fingerprint recognition instead of using passwords. In this proposed system face recognition is done using Convolutional Neural Network (CNN) and fingerprint images are compared using a fingerprint matching algorithm called Discrete Wavelet Transform (DWT). In recent days’ cloud domain gains a lot of user attention to store and access the data from remote locations connected through the internet. As it is generally known that all the sensitive data come from remote locations will be stored in the centralized storage medium and then try to access the data from that centralized storage space controlled by the untrusted cloud server. Authentication is done by using passwords, and PINs. It has grown in popularity over the years as a result of its use in areas like airports, secure financial transactions, banking, and mobile and computer access. Biometric is nothing but acquiring biological features of a person and making it as an identity for authentication. The proposed system combines two important biometrics, face recognition using Convolutional Neural Network and fingerprint recognition using Discrete Wavelet Transform (DWT) for untrusted cloud server authentication using biometric like face, fingerprint, iris, etc. The preliminary results demonstrate that Face Login is feasible, shows consistent superior performance compared to a set of other methods.