Abstract Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method strongly relies on the strength of extracted features and the ability to deal with low-quality face images. The proficiency to learn robust features from raw face images makes deep convolutional neural networks (DCNNs) attractive for face recognition. The DCNNs use softmax for quantifying model confidence of a class for an input face image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with available training examples. The primary goal of this paper is to improve the efficacy of face recognition systems by dealing with false positives through employing model uncertainty. Results of experimentations on open-source datasets show that 3–4% of accuracy is improved with model uncertainty over the DCNNs and conventional machine learning techniques.