Applied multivariate statistical analysis
Applied multivariate statistical analysis
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Robust Real-Time Face Detection
International Journal of Computer Vision
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web image mining towards universal age estimator
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Generic vs. person specific active appearance models
Image and Vision Computing
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Age Synthesis and Estimation via Faces: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Revisiting Linear Discriminant Techniques in Gender Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.