The identity or verification of humans primarily based on their thermal information isn't always an easy mission to perform, but thermal face biometrics can make contributions to that undertaking. Face reputation is an interesting and a successful application of Image analysis and Pattern recognition. Facial pictures are important for intelligent vision based human machine interaction. Face processing is based at the fact that the records approximately a consumer’s identity may be extracted from the image and the computers can act as a consequence. A thermal face image should be represented with biometrics features that highlight thermal face characteristic and are compact and easy to use for classification. Second, image resolution is basically lower for video sequences. If the subject is present in very far from the camera, the actual face image resolution can be as low as 64 by 64 pixels. Finally, face image variations, such as illumination, expression, pose, occlusion, and motion, are more important in video sequences. The approach can address the unbalanced distributions between still images and videos in a robust way by generating multiple “bridges” to connect the still images and video frames. So in this project, implement still to video matching approach to match the images with videos using Grassmann manifold learning approach to know unknown matches. Finally provide voice alert at the time unknown matching in real time environments. And implement neural network classification algorithms to classify the face images in real time captured videos.
Recognition of peripherally exposed emotional expressions
