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Understanding and Comparing Deep Neural Networks for Age and Gender Classification

 
: Samek, W.; Binder, A.; Lapuschkin, S.; Müller, K.-R.

:

Ikeuchi, K. ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Computer Vision Workshops, ICCVW 2017 : 22-29 October 2017, Venice, Italy. Proceedings
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-1034-3
ISBN: 978-1-5386-1035-0
S.1629-1638
International Conference on Computer Vision (ICCV) <2017, Venice>
Englisch
Konferenzbeitrag
Fraunhofer HHI ()

Abstract
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image preprocessing, model initialization and architecture choice. We present a study investigating these different effects. In detail, our work compares four popular neural network architectures, studies the effect of pretraining, evaluates the robustness of the considered alignment preprocessings via cross-method test set swapping and intuitively visualizes the model's prediction strategies in given preprocessing conditions using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our evaluations on the challenging Adience benchmark show that suitable parameter initialization leads to a holistic perception of the input, compensating artefactual data representations. With a combination of simple preprocessing steps, we reach state of the art performance in gender recognition.

: http://publica.fraunhofer.de/dokumente/N-520504.html