Biometrics are safer and more convenient than conventional authentication methods, including vulnerable passwords and ID cards. A unimodal biometric system has also been rapidly evolving in its precision, as part of biometric systems. However, every unimodal biometric feature has a disadvantage, such as loud sensed information, variations intraclasses, absence of individuality, non-universality, and spoof attack. These constraints have established the maximum efficiency of unimodal systems. This means that the research community is developing solid and guaranteed biometric systems that are harder to delude than systems based on a single biometrics. During the classification phase, the neural network (MLP) is explored for robust decision in the presence of slight variations and noise. The feasibility of all these algorithms has been successfully tested. It has been shown that biomodal biometric systems are more accurate and noise robust than unimodal system. Compared with unimodal structures, the suggested bimodal biometric technologies generate promising and improved outcomes.