Abstract
Face recognition has been widely used in areas such as security informatics, forensic investigation, customer tracking and mobile payment. This project is inspired by a photography artwork by Francois Brunelle, where he spent 12 years tracking people who are completely strangers but lookalikes, or doppelgangers. We aim to use face recognition techniques to mine doppelgangers on a school campus. We developed a face processing system which includes four steps, face detection, image processing (alignment and cropping), feature extraction and classification. We trained Multi-task Cascaded Convolutional Networks (MTCNN) and traditional CNNs with joined softmax loss and Center Loss on the Caffe framework. Finally, cosine similarity is used to detect similar faces. By exhibiting the results, we demonstrate the potential to adopt CV technology in art-related domains, in this case mimicking a photographer’s human eyes. This project provides an example for cross-disciplinary study between art and technology and will inspire researchers from both domains to establish further collaboration channels.
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Ye, J., Zhou, Y. (2020). Finding Real-Life Doppelgangers on Campus with MTCNN and CNN-Based Face Recognition. In: Lang, K.R., et al. Smart Business: Technology and Data Enabled Innovative Business Models and Practices. WeB 2019. Lecture Notes in Business Information Processing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-030-67781-7_7
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