In this work, a new method for human re-identification (Re-ID) from multiple surveillance cameras is proposed. Unlike traditional methods in which only the body features are used for matching, our proposed method uses both body and facial features for the Re-ID process. This combination allows us to re-identify people in challenging conditions, such as people with a uniform or similar-looking outfit, a partial or occluded body, appearance changes, or illumination. The face and body feature extraction models were developed using the state-of-the-art deep neural backbones and the synthesis of existing datasets in the literature. The performance of the method was evaluated on a self-generated dataset, which contains images under challenging conditions. The evaluation results show that our method outperforms traditional methods, in which the accuracy Rank1 reaches 91.30% while the traditional ones have a Rank1 of only 86.96%. This newly introduced method can be used for many practical applications in security surveillance of buildings and offices where challenging conditions often appear