Loading...
Convnet features for age estimation
Bukar, Ali M. ; Ugail, Hassan
Bukar, Ali M.
Ugail, Hassan
Publication Date
2017-07
End of Embargo
Supervisor
Rights
Peer-Reviewed
Yes
Open Access status
closedAccess
Accepted for publication
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
Research in facial age estimation has been active for over a decade. This is due to its numerous applications. Recently, convolutional neural networks (CNNs) have been used in an attempt to solve this age old problem. For this purpose, researchers have proposed various CNN architectures. Unfortunately, most of the proposed techniques have been based on relatively ‘shallow’ networks. In this work, we leverage the capability of an off-the-shelf deep CNN model, namely the VGG-Face model, which has been trained on millions of face images. Interestingly, despite being a simple approach, features extracted from the VGG-Face model, when reduced and fed into linear regressors, outperform most of the state-of-the-art CNNs. e.g. on both FGNET-AD and Morph II benchmark databases. Furthermore, contrary to using the last fully connected (FC) layer of the trained model, we evaluate the activations from different layers of the architecture. In fact, our experiments show that generic features learnt from intermediate layer activations carry more ageing information than the FC layers.
Version
No full-text in the repository
Citation
Bukar AM and Ugail H (2017) Covnet features for age estimation. Presented at: The 11th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing,
21–23 July 2017, Lisbon, Portugal.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Conference paper