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Poster
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
Zhenyao Zhu · Ping Luo · Xiaogang Wang · Xiaoou Tang

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D #None
in Poster Session »

Various factors, such as identities, views (poses), and illuminations, are coupled in face images. Disentangling the identity and view representations is a major challenge in face recognition. Existing face recognition systems either use handcrafted features or learn features discriminatively to improve recognition accuracy. This is different from the behavior of human brain. Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes. In this sense, human brain has learned and encoded 3D face models from 2D images. To take into account this instinct, this paper proposes a novel deep neural net, named multi-view perceptron (MVP), which can untangle the identity and view features, and infer a full spectrum of multi-view images in the meanwhile, given a single 2D face image. The identity features of MVP achieve superior performance on the MultiPIE dataset. MVP is also capable to interpolate and predict images under viewpoints that are unobserved in the training data.

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Author Information

Zhenyao Zhu (the Chinese University of Hong Kong)
Ping Luo (The University of Hong Kong)
Xiaogang Wang (The Chinese University of Hong Kong)
Xiaoou Tang (Chinese University of Hong Kong)

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