Towards Improved Cartoon Face Detection and Recognition Systems
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Given the significant advancement in face detection and recognition techniques for human faces over recent years, we questioned how well would they work for cartoon faces - a domain that remains largely unexplored yet, mainly due to the unavailability of abundant data sets and failure of traditional methods on these. In the present article, we employ various state-of-the-art deep learning frameworks for detecting and recognizing faces of cartoon characters along with proposing a novel approach to cartoon face recognition. For face detection, our work demonstrates the effectiveness of the Multi-task Cascaded Convolutional Network (MTCNN) architecture and contrasts it with other benchmark methods. For face recognition, we present two feature-based techniques: (i) an Inductive transfer approach combining the feature learning capability of the Inception v3 network and feature recognizing capability of Support Vector Machines (SVM), (ii) a proposed Hybrid Convolutional Neural Network (HCNN) based recognition framework trained over a fusion of pixel values and 15 manually located facial key points. All the methods are evaluated on the Cartoon Faces in the Wild (IIIT-CFW) database. We show a detailed analysis of the performance of the models using several metrics over a number of input constraints. Experiments show that the MTCNN based model results in a respective gain of 3.97 positive rate and False negative rate over the state-of-the-art detection method while both the recognition models surpass the state-of-the-art in terms of F-score. The Inception v3+SVM recognition model also establishes a new benchmark F-score of 0.910 on the task of cartoon gender recognition. We also introduce a small sized database containing location coordinates of 15 key points of the cartoon faces belonging to 50 public figures included in the IIIT-CFW database.
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