Recognition of facial expression is a potential application in emotion analysis. This research is mainly focused to study the emotion cues hidden in human face. Even a slightest anxiety or excitement can trigger a warm spread across the cheeks. Similarly, face temperature plummets when a person is shaken by a loud noise. Thus thermal facial images are used in this research to analyze the emotions using the heat map spread across the face. The recognition system is framed and designed on the concept of visible spectrum. The new concept greatly affects the recognition method to be effective on poorly illuminated environments. In this proposed method which is called as the Modified Thermal Emotion Recognition we use the Eigen face technique which is of great help when it comes to a large database of faces. We also use PCA-principal component analysis with Eigen face because of its simplicity, speed and learning capability. The PCA technique helps in efficiently representing pictures of faces along with the Eigen face technique. Once the weights are derived from the original images, ADA Boost algorithm is applied, which helps to reduce huge image database. ADA Boost simplifies and reduces the number of weights and helps in easy calculation process. SIFT and GLCM algorithm is used to extract the features from the image by training the database.