TISVM: Large margin classifier for misaligned image classification

Support vector machine is one of the most successful machine learning methods in image processing and computer vision in the past decades. However, its performance strongly depends on the training data, which are sometimes expensive and of low quality. Specifically, in many real applications, such as face recognition, the images are rarely perfectly aligned, thus the misalignment between training and testing data impairs the performance. In this paper, we propose a strategy to compensate the misalignment between images while learning the classifier without looking at the testing samples. Specifically, some certain critical transformations are inferred and applied to training samples to alleviate the effect of the worst case of possible misalignment. The resulted large margin classifier generalizes better than traditional SVM, especially when there is misalignment. Experimental results on real image data sets show the efficacy of the proposed algorithm.