Ilmini, WMKS & Fernando, TGI (2023). “Explaining the Outputs of Convolutional Neural Network – Recurrent Neural Network (CNN-RNN) Based Apparent Personality Detection Models Using the Class Activation Maps.” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 2, Feb. 2023, https://thesai.org/Publications/ViewPaper?Volume=14&Issue=2&Code=IJACSA&SerialNo=24.
Abstract:
This study aims to use the Class Activation Map (CAM) visualisation technique to understand the outputs of apparent personality detection models based on a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The ChaLearn Looking at People First Impression (CVPR’17) dataset is used for experimentation in this study. The dataset consists of short video clips labelled with the Big Five personality traits. Two deep learning models were designed to predict apparent personality with VGG19 and ResNet152 as base models. Then the models were trained using the raw frames extracted from the videos. The highest accurate models from each architecture were chosen for feature visualisation. The test dataset of the CVPR’17 dataset is used for feature visualisation. To identify the feature’s contribution to the network’s output, the CAM XAI technique was applied to the test dataset and calculated the heatmap. Next, the bitwise intersection between the heatmap and background removed frames was measured to identify how much features from the human body (including facial and non-facial data) affected the network output. The findings revealed that nearly 35%-40% of human data contributed to the output of both models. Additionally, after analysing the heatmap with high-intensity pixels, the ResNet152 model was found to identify more human-related data than the VGG19 model, achieving scores of 46%-51%. The two models have different behaviour in identifying the key features which influence the output of the models based on the input.