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.
