Fuzzy Support Vector Machines for Face Recognition: A Review

Support vector machine (SVMs) is a classical classification tool in face recognition. In ordinary SVM, every input points are considered to have the same commitment to the training model. On the other hand, this is not generally valid due to some challenges in face recognition. Since there may be a few points undermined by commotion so they are less significant and the machine ought to better to toss them which are undecidable. This paper review some methodology to handle this sort of information giving so as to utilize fuzzy methodology them a weight which demonstrate the diverse commitment of every point to the model. The weights are resolved as for their membership function. Such approach is typically called as Fuzzy SVM (FSVM).