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Now I am using multiple SVM with SURF features for face recognition. For example I have 3 types of images and each type I called as class1,calss2 and class3. So I build SVM1 as combination of class1 and class2 where class1 images are labeled as +1 and class2 images labeled as -1 like that SVM2 is the combination of class2 and class3 where class2 images are labeled as +1 and class3 images labeled as -1 and SVM3 is the combination of class3 and class1 where class3 images are labeled as +1 and class1 images labeled as -1. So at the time of prediction if the given image is in any of the class then that particular SVM return +1 and I concluded that image s "known", if all SVM return -1 then it is "Unknown". But now my problem is, if the given image is not in the training set then also SVM return +1 and identified as known one.Actually it get drops my system accuracy level. Is there any problem in my approach? why I got false results? Please help me.