Human behavior prediction using facial expression analysis

Computer Vision seeks to emulate human vision by analyzing digital image inputs as human perception does. To detect an emotion will not be a difficult task to for human but for any computer, detecting an emotion will be a difficult job to perform as they are unaware of that human nature. In our paper, first of all, we converted a video into frame sequence which is used for human behavior prediction by correlating frames. To analyze face, a face is detected and the ROI for expression using Viola-Jones AdaBoost method on color filtered image algorithm to detect people's faces, noses, eyes, mouth, or upper body, as these parts have higher entropy for emotion detection. We used a PCA algorithm for feature extraction which calculates coefficient, score, and latent; where latent is used to calculate Eigenvector. As we found that left to right and top to the bottom approach to facial movement gives facial gesture change and facial expression change respectively and so on. For human emotion prediction, we used Euclidian distance calculation. From the various sequence of emotion, appropriate behavior is predicted. Finally, we have developed an algorithm for continuous tracking and monitoring the moving suspicious faces continuously using Kalman filter estimation. The proposed method is simple and very useful for the high-security alert zones e.g. hospital, airport, bank etc.