Face verification experiments on the LFW database with simple features, metrics and classifiers

Inhalt:
Face verification is fast becoming a dual platform for testing face recognition algorithms. Unlike recognition, verification is non-intrusive and more practical for security applications, where it is more important to look for match or mismatch between faces, rather than the exact identity. This paper aims at providing comprehensive overwiev af the verification accuracies that can be achieved through the use of simple techniques and out-of-the-box methods. Experiments are performed with features, metrics and learning methods from different categories, i.e, global an local features, generative and discriminative models, and results are appropriately reported. The main contribution of this paper is to provide a starting point for future researchers in the field of face recognition and verification.