Age Regression from Soft Aligned Face Images Using Low Computational Resources


The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

DOI: 10.1007/978-3-642-21257-4_35

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@inproceedings{BekiosCalfa2011AgeRF, title={Age Regression from Soft Aligned Face Images Using Low Computational Resources}, author={Juan Bekios-Calfa and Jos{\'e} Miguel Buenaposada and Luis Baumela}, booktitle={IbPRIA}, year={2011} }