Everything is in the Face? Represent Faces with Object Bank

Abstract

Abstract. 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).

VIPLFaceNet: an open source deep face recognition SDK

“…In [9] and [10], filters are learned to maximize the discriminative power for face recognition. In [21], faces are represented from its responses to many pre-trained object filters. In [11], [12], [13] and [22], codebook learning technologies are utilized for robust face representation.…”