Hi,
I'm using block-based uniform LBP as feature and training an SVM for gender clarification on face images.
My 1st trained SVM model is computed from 1200 male face images and 500 female face images. (My CvSVMParams setting is exactly the same as the OpenCV SVM example in http://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html The result is not good and the hit rate is only about 6x%.
Then I try to improve the hit rate by adding more face images for training. I use this 1st trained SVM model to predict more face images and use the wrong classified ones as my additional training face images. So my 2nd trained SVM model is computed from 1200+200 male face images and 500+100 female face images. I'm expecting the second SVM model works better than the first one. However, the second one is always overfitted...
I'm wondering is there any other way to improve the hit rate and why my approach gets more inaccurate classified result. Hope anyone could kindly provide me some hints. Thanks.