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From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter. This has motivated a renewed interest in building datasets which are socially and culturally relevant, so that algorithmic research may have a more direct and immediate impact on society. One such area is in history and the humanities, where better and relevant machine learning models can accelerate research across various fields. To this end, newly released benchmarks and models have been proposed for transcribing historical Japanese cursive writing, yet for the field as a whole using machine learning for historical Japanese artworks still remains largely uncharted. To bridge this gap, in this work we propose a new dataset KaoKore which consists of faces extracted from pre-modern Japanese artwork. We demonstrate its value as both a dataset for image classification as well as a creative and artistic dataset, which we explore using generative models. Dataset available at https://github.com/rois-codh/kaokore
Facial expression recognition from videos in the wild is a challenging task due to the lack of abundant labelled training data. Large DNN (deep neural network) architectures and ensemble methods have resulted in better performance, but soon reach sat
Face images are subject to many different factors of variation, especially in unconstrained in-the-wild scenarios. For most tasks involving such images, e.g. expression recognition from video streams, having enough labeled data is prohibitively expen
Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of
One of the most common problems encountered in human-computer interaction is automatic facial expression recognition. Although it is easy for human observer to recognize facial expressions, automatic recognition remains difficult for machines. One of
In this paper, we target on advancing the performance in facial expression recognition (FER) by exploiting omni-supervised learning. The current state of the art FER approaches usually aim to recognize facial expressions in a controlled environment b