Title :
A Fair Comparison Should Be Based on the Same Protocol--Comments on "Trainable Convolution Filters and Their Application to Face Recognition"
Author_Institution :
Coll. of Math. & Inf. Sci., Wenzhou Univ. (adjunct), Wenzhou, China
Abstract :
We comment on a paper describing an image classification approach called Volterra kernel classifier, which was called Volterrafaces when applied to face recognition. The performances were evaluated by the experiments on face recognition databases. We find that their comparisons with the state of the art of three databases were indeed based on unfair settings. The results with the settings of the standard protocol on three data sets are generated, which show that Volterrafaces achieves the state-of-the-art performance only in one database.
Keywords :
convolution; face recognition; filtering theory; image classification; visual databases; Volterra kernel classifier; Volterrafaces; face recognition databases; image classification approach; trainable convolution filter; Computer vision; Face recognition; Kernel; Protocols; Standards; Training; Face recognition; Volterra kernels; Volterrafaces; filtering classifier;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2013.187