  Given the recent deep learning advancements in face detection and recognition
techniques for human faces, this paper answers the question "how well would
they work for cartoons'?" - a domain that remains largely unexplored until
recently, mainly due to the unavailability of large scale datasets and the
failure of traditional methods on these. Our work studies and extends multiple
frameworks for the aforementioned tasks. For face detection, we incorporate the
Multi-task Cascaded Convolutional Network (MTCNN) architecture and contrast it
with conventional methods. For face recognition, our two-fold contributions
include: (i) an inductive transfer learning approach combining the feature
learning capability of the Inception v3 network and the feature recognizing
capability of Support Vector Machines (SVMs), (ii) a proposed Hybrid
Convolutional Neural Network (HCNN) framework trained over a fusion of pixel
values and 15 manually located facial keypoints. All the methods are evaluated
on the Cartoon Faces in the Wild (IIIT-CFW) database. We demonstrate that the
HCNN model offers stability superior to that of Inception+SVM over larger input
variations, and explore the plausible architectural principles. We show that
the Inception+SVM model establishes a state-of-the-art F1 score on the task of
gender recognition of cartoon faces. Further, we introduce a small database
hosting location coordinates of 15 points on the cartoon faces belonging to 50
public figures of the IIIT-CFW database.
