Preprocessing Challenges for Real World Affect Recognition

Abstract

Real world human affect recognition requires immediate attention which is a significant aspect of humancomputer interaction. Audio-visual modalities can make a significant contribution by providing rich contextual information. Preprocessing is an important step in which the relevant information is extracted. It has a crucial impact on prominent feature extraction and further processing. The main aim is to highlight the challenges in preprocessing real world data. The research focuses on experimental testing and comparative analysis for preprocessing using OpenCV, Single Shot MultiBox Detector (SSD), DLib, Multi-Task Cascaded Convolutional Neural Networks (MTCNN), and RetinaFace detectors. The comparative analysis shows that MTCNN and RetinaFace give better performance in real world data. The performance of facial affect recognition using a pre-trained CNN model is analysed with a lab-controlled dataset CK+ and a representative wild dataset AFEW. This comparative analysis demonstrates the impact of preprocessing issues on feature engineering framework in real world affect recognition.