Appearance can affect social interaction, which in turn affects personality development. There is ample evidence that facial morphology and social cues provide information about human personality and behavior. In this study, we focused on the relationship between self-reported personality characteristics and facial features. We propose a new approach for predicting college students’ personality characteristics (on the basis of the Big Five personality characteristics) with static facial images. First, we construct a dataset containing 13,347 data pairs composed of facial images and personality characteristics. Second, we train a deep neural network with 10,667 sample pairs from the dataset and use the remaining samples to test (1335 pairs) and validate (1335 pairs) self-reported Big Five personalities. We trained a series of deep neural networks on a large, labeled dataset to predict the self-reported Big Five personality trait scores. This novel work applies deep learning to this topic. We also verify the network’s advanced nature on the publicly available database with obvious personality characteristics. The experimental results show that 1) personality traits can be reliably predicted from facial images with an accuracy that exceeds 70%. In five-character tag classification, the recognition accuracy of neuroticism and extroversion was the most accurate, and the prediction accuracy exceeded 90%. 2) Deep learning neural network features are better than traditional manual features in predicting personality characteristics. The results strongly support the application of neural networks trained on large-scale labeled datasets in multidimensional personality feature prediction from static facial images. 3) There are some differences in the personality traits of college students with different academic backgrounds. Future research can explore the relative contribution of other facial image features in predicting other personality characteristics.