Identifying Individual Facial Expressions by Deconstructing a Neural Network
By Farhad Arbabzadah, Grgoire Montavon, K. Müller, Wojciech Samek. 2016
31 citations recorded by the source corpus.
This paper focuses on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks. Since psychological attribute datasets typically suffer from small sample sizes, we apply transfer learning with two base models to avoid overfitting. These models were trained on an age and gender prediction task, respectively. Using a novel explanation method we extract heatmaps that highlight the parts of the image most responsible for the prediction. We further observe that the explanation method provides important insights into the nature of features of the base model, which allow one to assess the aptitude of the base model for a given transfer learning task. Finally, we observe that the multiclass model is more feature rich than its binary counterpart. The experimental evaluation is performed on the 2222 images from the 10k US faces dataset containing psychological attribute labels as well as on a subset of KDEF images.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Methods for interpreting and understanding deep neural networks (2017, methodology): “[2] to reduce stride artifacts when explaining facial expression data with LRP.”
- Unmasking Clever Hans predictors and assessing what machines really learn (2019, methodology): “Explanation methods were successfully applied to a wide set of complex real-world problems such as the analysis of faces [81, 123, 124], EEG and fMRI data [23, 50], human gait [48], videos [125], speech [126], reinforcement learning [25], biological data [127], text [26,128,129], or comparing human ”
- Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation (2016, methodology): “In [16] it was used for the identification of relevant words in text documents and in [17] for visualizing facial features related to age, happiness and attractivity.”