Estimating in-plane rotation angle for face images from multi-poses

Classical face detection algorithm works on only near frontal faces. Extending it to other poses and in-plane rotated faces require separately trained classifiers which increases both the training and processing time. We solve this instead by developing a reference model that is capable of detecting...

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Bibliographic Details
Main Authors: Anvar, Seyed Mohammad Hassan, Wei-Yun Yau, Nandakumar, Karthik, Eam Khwang Teoh
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Detectors
Face
Face detection
Feature extraction
in-plane rotation invariant
Labeling
multi-view faces
pose invariant
rotation angle estimation
Training
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Description
Summary:Classical face detection algorithm works on only near frontal faces. Extending it to other poses and in-plane rotated faces require separately trained classifiers which increases both the training and processing time. We solve this instead by developing a reference model that is capable of detecting upright faces in various poses. Then a probabilistic framework is used to estimate occurrence of in-plane rotated faces. Experimental results showed that the proposed approach can achieve face detection accuracy comparable to state-of-the-art approaches but returns more accurate in-plane rotation angle estimation and is much faster. Unlike other approaches, the proposed method is easy to train, requiring only a small number of images and only one manually labeled face image.
ISSN:2325-4300
DOI:10.1109/CIBIM.2013.6607914