Linear Ensemble Methods for Multiclass Discrimination

We consider the case of a discrimination problem for which several classifiers are available. The object of this study is the combination of the class posterior probability estimates provided by these classifiers, in order to obtain better estimates and concomitantly a better discriminant function. The ensemble method is the multivariate linear regression. We specify the optimization problem to which training amounts and we characterize the sets of optimal solutions corresponding to the main convex objective functions. Means to assess and control the generalization capabilities are also investigated

The impact of bio-inspired approaches toward the advancement of face recognition

An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come