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International Journal of Computer Vision

Erschienen in:

01.05.2015

Discriminative Deep Face Shape Model for Facial Point Detection

verfasst von: Yue Wu, Qiang Ji

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2015

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Abstract

Facial point detection is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since facial shapes vary significantly with facial expressions, poses or occlusion. In this paper, we address this problem by proposing a discriminative deep face shape model that is constructed based on an augmented factorized three-way Restricted Boltzmann Machines model. Specifically, the discriminative deep model combines the top-down information from the embedded face shape patterns and the bottom up measurements from local point detectors in a unified framework. In addition, along with the model, effective algorithms are proposed to perform model learning and to infer the true facial point locations from their measurements. Based on the discriminative deep face shape model, 68 facial points are detected on facial images in both controlled and “in-the-wild” conditions. Experiments on benchmark data sets show the effectiveness of the proposed facial point detection algorithm against state-of-the-art methods.

Vorheriger Artikel Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network

Nächster Artikel Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

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Titel: Discriminative Deep Face Shape Model for Facial Point Detection
verfasst von: Yue Wu
Qiang Ji
Publikationsdatum: 01.05.2015
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2015
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-014-0775-8

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