Understanding and Comparing Deep Neural Networks for Age and Gender Classification
This work addresses the lack of interpretability in deep learning models for facial analysis, providing insights for researchers and practitioners in computer vision, though it is incremental in nature.
The study investigated how image preprocessing, model initialization, and architecture choice affect deep neural networks for age and gender classification, achieving state-of-the-art performance in gender recognition on the Adience benchmark through a combination of simple preprocessing steps.
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.