This work presents a model based on Deep Neural Networks for the prediction of apparent personality. It can quantify personality traits with the Five-Factor model (Big Five) from a Portrait image. In order to evaluate the effectiveness of this approach, a new corpus of 30,935 portraits with their associated personality trait was extracted from an existing resource of videos (First Impressions, ChaLearn) tagged with redundant pairwise comparisons to ensure consistency. We propose several models using Convolutional Neural Networks to automatically extract features from a portrait that are indicators of personality traits; then the models classify these characteristics into a binary class for each Big Five factor: openness to experience (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). In addition, we experiment with feature encoding and transfer learning to enrich the representation of images with additional untagged portraits (∼45,000 and ∼200M), reaching a percentage of accuracy within the state of the art (albeit not directly comparable), obtaining 65.86% as a classifier averaging the 5 factors (O=61.48%, C=69.56%, E=73.23%, A=60.68%, N=64.35%). Compared to human judgment (mean accuracy of 56.66%), the model obtained higher average performance and higher accuracy in 4 of the 5 factors of the Big Five model. In addition, in comparison with the state of the art this model shows several advantages: (1) it requires only a single portrait to make the prediction, being this a non-invasive and easily accessible resource (e.g. selfies) (2) the extraction of features from the portrait is done automatically, (3) a single model performs the extraction of characteristics and classification.

A policy determines the action that an autonomous car needs to take according to its current situation. For example, the car keeps itself on track or overtakes another car, among other policies. Some autonomous cars could need more than one policy to drive appropriately. In those systems, the behavior selector subsystem selects the policy that the car needs to follow. However, in the current literature, there is not a unified way to create these policies. In most cases, the amount and definition of the policies are hand-engineering using the information taken from observations and the knowledge of the engineers. That paradigm requires a lot of human effort. Additionally, there is human subjectivity due to the hand labeling. Furthermore, the experts could not agree about the number of existing situations and the boundaries between policies (the point at which one situation turns into another). To deal with the subjectivity of setting the number and definition of policies, we propose a novel approach that uses the “divide and conquer” paradigm. This method first, sets the number of required policies by clustering previous observations into situations, and second, it configures a regression-based policy for each situation. As a result, (i) the method can detect driving situations from raw data automatically using unsupervised algorithms, helping to avoid the hand-engineering made by an expert, and (ii) the method creates relatively small and efficient policies without human intervention using behavioral cloning. To validate the method, we have collected a custom dataset in simulation and we have conducted several experiments comparing the performance of our proposal versus two state-of-the-art end-to-end methods. Our results show that our method outperforms the end-to-end approaches in terms of a bigger R square metric (0.19 over the tested methods) and a lower mean squared error (0.48 below the tested methods).