Automatic enrollment involves a critical decision-making process within people re-identification context. However, this process has been traditionally undervalued. This paper studies the problem of automatic person enrollment from a realistic perspective relying on gait analysis. Experiments simulating random flows of people with considerable appearance variations between different observations of a person have been conducted, modeling both short- and longterm scenarios. Promising results based on ROC analysis show that automatically enrolling people by their gait is affordable with high success rates.
This paper presents a statistical study of local vs. global approaches for classifying gender from neutral and expressive faces. A cross-dataset evaluation is provided by using different training and test face databases, as well as several well-known classifiers (1-NN, PCA+LDA and SVM) and widely used features for facial description. Three statistical tests have proved that local approaches are more suitable than global ones for solving gender classification problems over expressive faces when training with non-expressive faces. However, if a large set of expressive faces is available for training, global solutions outperform local ones.