Automatic age estimation from facial profile view - Ali Maina Bukar; Hassan Ugail (IET Computer Vision, 2017)
In recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side‐view of face images. Here the authors exploit this by using a pretrained deep residual neural network to extract features, and then utilise a sparse partial least‐squares regression approach to estimate ages. Despite having less information as compared with frontal images, the results show that the extracted deep features achieve a promising performance.