Bi-Modality Anxiety Emotion Recognition with PSO-CSVM

Abstract

In Human Computer Interaction (HCI) community, the ultimate goal which we are striving to achieve is to create a natural, harmony way of bi-directional communication between machine and human. As we well known, many machine intelligence applications are based on machine vision technology, for instance, industrial detection, face recognition, medical image computer aided diagnose (MICAD), fingerprint recognition, and so on. In terms of artificial intelligence, it is built upon digital image processing and video frame analyzing to extract feature information to recognize some interesting objects. Moreover, a high-level machine learning system should be capable of identifying human emotion states and make interactions accordingly. Multimodal human emotion recognition involving facial expression and motion recognition could be applied to intelligent video surveillance system to provide an early warning mechanism in case of potential unsafe action occurring. There is no doubt that the ever increasing various kinds of crimes in our modern society which make the living environment around us even worse, demand for an intelligent and automatic security precautions measures to offer people a more convenient, relaxed living conditions. Meanwhile, automatic intelligent video-based surveillance system has received a lot of interest in the computer vision and human computer interaction community in recent years. CMU’s Video Surveillance and Monitoring (VSAM) project [26] and MIT AI Lab’s Forest of Sensors project [27] are examples of recent research efforts in this field. As a matter of fact, the safeguard has to keep watch on lots of screens in control center in real application. The video displayed on the screens are captured from cameras which are distributed in various security-sensitive areas such as elevator, airports, railway station or public places. However, because of human’s inherent information processing limitation, it is impossible to pick up all of the useful information from the monitors at the same time properly and spontaneously. That means some potentially dangerous information could be missed out. We expect the human computer interaction system is able to take the information-processing burden off the human. An ideal and effective intelligent surveillance system should work automatically without or with minimal human intervention. In addition, we believe that an intelligent surveillance system should be capable of preventing and predict criminal occurring by biometric recognition, rather than identifying the suspect after attacks happened. Figure 1 shows some pictures in which the subjects are under the anxiety or stress emotion state when she interviewed with human resource manager, not criminal nevertheless.