  Deep learning system have drawback that their output is not accompanied with
ex-planation. In a domain such as forensic handwriting verification it is
essential to provideexplanation to jurors. The goal of handwriting verification
is to find a measure of confi-dence whether the given handwritten samples are
written by the same or different writer.We propose a method to generate
explanations for the confidence provided by convolu-tional neural network (CNN)
which maps the input image to 15 annotations (features)provided by experts. Our
system comprises of: (1) Feature learning network (FLN),a differentiable
system, (2) Inference module for providing explanations. Furthermore,inference
module provides two types of explanations: (a) Based on cosine
similaritybetween categorical probabilities of each feature, (b) Based on
Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We
perform experiments using acombination of feature learning network (FLN) and
each inference module. We evaluateour system using XAI-AND dataset, containing
13700 handwritten samples and 15 cor-responding expert examined features for
each sample. The dataset is released for publicuse and the methods can be
extended to provide explanations on other verification taskslike face
verification and bio-medical comparison. This dataset can serve as the basis
and benchmark for future research in explanation based handwriting
verification. The code is available on github.
