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From pixels to response maps: discriminative image filtering for face alignment in the wild

Asthana, Akshay; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Cheng, Shiyang; Pantic, Maja

Authors

Akshay Asthana

Stefanos Zafeiriou

Georgios Tzimiropoulos

Shiyang Cheng

Maja Pantic



Abstract

We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advantages. Firstly, by virtue of discriminative training, invariance to external variations (like identity, pose, illumination and expression) is achieved. Secondly, we show that the responses generated by discriminatively trained filters (or patch-experts) are sparse and can be modeled using a very small number of parameters. As a result, the optimization methods based on the proposed texture model can better cope with unseen variations. We illustrate this point by formulating both part-based and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple ”wild” databases. The code and dataset annotations are available for research purposes from http://ibug.doc.ic.ac.uk/resources.

Journal Article Type Article
Publication Date Oct 9, 2014
Journal IEEE Transactions on Pattern Analysis and Machine Intelligence
Print ISSN 0162-8828
Electronic ISSN 0162-8828
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 37
Issue 6
APA6 Citation Asthana, A., Zafeiriou, S., Tzimiropoulos, G., Cheng, S., & Pantic, M. (2014). From pixels to response maps: discriminative image filtering for face alignment in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(6), doi:10.1109/TPAMI.2014.2362142
DOI https://doi.org/10.1109/TPAMI.2014.2362142
Publisher URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6919301&tag=1
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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