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Given the recent deep learning advancements in face detection and recognitiontechniques for human faces, this paper answers the question "how well wouldthey work for cartoons'?" - a domain that remains largely unexplored untilrecently, mainly due to the unavailability of large scale datasets and thefailure of traditional methods on these. Our work studies and extends multipleframeworks for the aforementioned tasks. For face detection, we incorporate theMulti-task Cascaded Convolutional Network (MTCNN) architecture and contrast itwith conventional methods. For face recognition, our two-fold contributionsinclude: (i) an inductive transfer learning approach combining the featurelearning capability of the Inception v3 network and the feature recognizingcapability of Support Vector Machines (SVMs), (ii) a proposed HybridConvolutional Neural Network (HCNN) framework trained over a fusion of pixelvalues and 15 manually located facial keypoints. All the methods are evaluatedon the Cartoon Faces in the Wild (IIIT-CFW) database. We demonstrate that theHCNN model offers stability superior to that of Inception+SVM over larger inputvariations, and explore the plausible architectural principles. We show thatthe Inception+SVM model establishes a state-of-the-art F1 score on the task ofgender recognition of cartoon faces. Further, we introduce a small databasehosting location coordinates of 15 points on the cartoon faces belonging to 50public figures of the IIIT-CFW database.

Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems