In this work, we propose an object detection training regime, that does not rely on bounding box labelled datasets, hence unsupervised in nature, and is solely based on trained image classifiers. We build up on our hypothesis, that, "if an image classifier is able to predict what object is in the input image, then it must have information about where the object is, we just need a mechanism to extract that information from it".

In this work, we try to propose a novel approach to touchless palmprint recognition using locally attentive CNNs. Its a CNN architecture with an image and its sub patches as input. With this methodology, we aim to reduce the inaccuracy of prediction of traditional methods by combining local and global image information into a single model. We use bilinear pooling to combine the feature descriptors of the local and global CNNs and use a inner product regularization while training.