In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.

Facial demographic classification is an attractive topic in computer vision. Attributes such as age and gender can be used in many real life application such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Phase Quantization (ML-LPQ) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on the benchmark Image of Groups dataset showed the superiority of our approach compared to the state-of-the-art.

In this paper, we propose a novel approach for estimating the Big Five personality traits and the job candidate screening attribute through facial videos. At running time, the proposed system feeds the Pyramid Multi-Level (PML) texture features extracted from the whole video sequence to 5 Support Vector Regressors in order to estimate the personality traits. These estimated five scores are then considered as new input features to the interview score regressor. The latter is given by a Gaussian Process Regression (GPR). The experimental results on ChaLearn LAP APA2016 dataset achieve good performance. Furthermore, they demonstrate that the computational cost of both the training and the testing of the proposed framework are very competitive in terms of accuracy and computational cost.