Abstract
Automatic head pose estimation consists of using a computer to predict the pose of a person based on a given facial image. Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. Many of machine learning-based techniques used for face detection and recognition can also be used for pose estimation. In this chapter, we present a new dimensionality reduction algorithm based on a sparse representation that takes into account pose similarities. Experimental results conducted on three benchmarks face databases are presented.
Keywords: Age classification, Age estimation, Age prediction, Dimensionality reduction, Facial feature extraction, Gabor filter, K-nearest neighbors, Labelsensitive, Local binary pattern, Local regression, Locality preserving projections, Machine learning, Marginal fisher analysis, Mean absolute error, Partial least square regression, Preprocessing, Recognition rate, Support vector regression.
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