The unimodal technique based on biometric validation depends on single feature or biological features for measurements and examination. The biological-based biometric validation based on biological feature is mostly popular still they are not safe to demonstrate attack and spoofing when there is a duplicate biological feature is sent to sensors. To overcome this issue, we have introduced an enhanced biometric validation that depends on multimodal method using two different types of biological traits. We have introduced a concatenate method to merge ECG and face traits to minimize unsafe attacks. Particularly, we have built a multimodal model that depends on deep learning that takes ECG and face traits as input and combine feature vectors accordingly. Further, we have used local binary pattern, the well-known and popular feature extraction technique used in image processing. The result of the introduced model shows the promising results in improving the effectiveness and robustness of system to various vulnerable attacks.