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

Asthana, Akshay, Zafeiriou, Stefanos, Tzimiropoulos, Georgios, Cheng, Shiyang and Pantic, Maja (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). pp. 1312-1320. ISSN 0162-8828

Full text not available from this repository.


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

Item Type: Article
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.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 29 Jan 2016 10:59
Last Modified: 04 May 2020 16:56

Actions (Archive Staff Only)

Edit View Edit View