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Automatic emotional state detection and analysis on embedded devices
This thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University LondonFrom the last decade, studies on human facial emotion recognition revealed that computing models based on regression modelling can produce applicable performance. In this study, an automatic facial expression real-time system was built and tested. The method is used in this study has been used widely in different areas such as Local Binary Pattern method, which has been used in many research projects in machine vision, and the K-Nearest Neighbour algorithm is method utilized for regression modelling. In this study, these two techniques has been used and implemented on the FPGA for the first time, on the side and joined together to great the model in such way to display a continues and automatic emotional state detection model on the monitor. To evaluate the effectiveness of the classifier technique for human emotion recognition from video, the model was designed and tested on MATLAB environment and then MATLAB Simulink environment that is capable of recognizing continuous facial expression in real time with a rate of 1 frame per second and implemented on a desktop PC. It has been evaluated in a testing dataset and the experimental results were promising with the accuracy of 51.28%. The datasets and labels used in this study are made from videos which, recorded twice from 5 participants while watching a video. In order to implement it in real-time in faster frame rate, the facial expression recognition system was built on FPGA. The model was built on Atlysâ„¢ Spartan-6 FPGA Development Board. It can perform continuously emotional state recognition in real time at a frame rate of 30 with the accuracy of 47.44%. A graphic user interface was designed to display the participant video in real time and also two dimensional predict labels of the emotion at the same time. This is the first time that automatic emotional state detection has been successfully implemented on FPGA by using LBP and K-NN techniques in such way to display a continues and automatic emotional state detection model on the monitor
Automatic depression scale prediction using facial expression dynamics and regression
Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behaviour, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.The work by Asim Jan was supported by School of Engineering & Design/Thomas Gerald Gray PGR Scholarship. The work by Hongying Meng and Saeed Turabzadeh was partially funded by the award of the Brunel Research Initiative and Enterprise Fund (BRIEF). The work by Yona Falinie Binti Abd Gaus was supported by Majlis Amanah Rakyat (MARA) Scholarship