Proceedings Article10.1109/AMFG.2003.1240846
Real-time view-based face alignment using active wavelet networks
Changbo Hu,Rogerio Feris,Matthew Turk +2 more
- 17 Oct 2003
- pp 215-221
TL;DR: This work extends the AWN method to a view-based approach, verifies the robustness of the algorithm with respect to unseen views in a large dataset, and shows that using only nine wavelets, the method yields similar performance to state-of-the-art face alignment systems, with a significant enhancement in terms of speed.
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Abstract: The active wavelet network (AWN) [C. Hu et al., (2003)] approach was recently proposed for automatic face alignment, showing advantages over active appearance models (AAM), such as more robustness against partial occlusions and illumination changes. We (1) extend the AWN method to a view-based approach, (2) verify the robustness of our algorithm with respect to unseen views in a large dataset and (3) show that using only nine wavelets, our method yields similar performance to state-of-the-art face alignment systems, with a significant enhancement in terms of speed. After optimization, our system requires only 3 ms per iteration on a 1.6 GHz Pentium IV. We show applications in face alignment for recognition and real-time facial feature tracking under large pose variations.
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Citations
Unsupervised Joint Alignment of Complex Images
Gary B. Huang,Vidit Jain,Erik Learned-Miller +2 more
- 26 Dec 2007
TL;DR: The alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images.
Facial Landmark Detection: a Literature Survey
TL;DR: In this paper, the authors classify the facial landmark detection algorithms into three major categories: holistic methods, constrained local model (CLM) methods, and regression-based methods, which differ in the ways to utilize the facial appearance and shape information.
255
Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders
Jie Zhang,Meina Kan,Shiguang Shan,Xilin Chen +3 more
- 27 Jun 2016
TL;DR: A novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem, which significantly outperforms the state-of-the-art methods.
Patent
Automated face enhancement
Michael F. Cohen,Jue Wang +1 more
- 14 Feb 2006
TL;DR: In this article, a face enhancement system and process which can automatically improve faces in videos or other images by applying cosmetic effects, given only a small amount of user interaction for initialization.
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Fast learning algorithm of wavelet network based on fast wavelet transform
TL;DR: A novel learning algorithm of wavelet networks based on the Fast Wavelet Transform (FWT) that is realized by iterative application of FWT to compute the connection weights and extended by using Levenberg–Marquardt method to optimize the learning functions.
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