Object Bank (OB) [1] has been recently proposed as an object-level image representation for high-level visual recognition. OB represents an image from its responses to many pre-trained object filters. While OB has been validated in general image recognition tasks, it might seem ridiculous to represent a face with OB. However, in this paper, we study this anti-intuitive potential and show how OB can well represent faces amazingly, which seems a proof of the saying that “Everything is in the face”. With OB representation, we achieve results better than many low-level features and even competitive to state-of-the-art methods on LFW dataset under unsupervised setting. We then show how we can achieve state of the art results by combining OB with some low-level feature (e.g. Gabor).
CITATION STYLE
Liu, X., Shan, S., Li, S., & Hauptmann, A. G. (2015). Everything is in the face? Represent faces with object bank. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9010, pp. 180–193). Springer Verlag. https://doi.org/10.1007/978-3-319-16634-6_14
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