Abstract— Facial expressions and emotions plays an important role in communications in social interactions with other human beings which delivers rich information about their mood. The “BLUE EYES TECHNOLOGY” aims at creating computational machines that have sensory and perceptual abilities like those of human beings which enables the computer to gather information about humans and interact with them. This paper implements the detection of emotions (happy, sad, fear, surprised, anger, disgust) by taking in consideration the human eye expressions and by using emotion mouse. The emotion mouse obtains physiological data and emotional state of a person through the single touch of mouse having different sensors. Emotions are also determined by human eye expression in which the eye region from a video sequence is analyzed. From the different frames of the video stream, the human eyes can be extracted using the edge operator and then can be classified using a Support Vector machine (SVM) classifier. After the classification we use standard learning tool, Hidden Markov Model (HMM) for recognizing the emotions from the human eye expressions. After successful detection of emotion, suitable audio track will be played. Keywords- Blue eyes, emotion mouse, emotion recognition, eye expressions, Support Vector Machine (SVM), Hidden Markov Model(HMM). I. INTRODUCTION The “BLUE EYES” technology aims at creating computational machines by adding extraordinary perceptual abilities to the computers that helps them to verify a person’s identity, feel their presence, and interact with them. Human recognition depends primarily on the stability to perceive, interpret, and integrate audio/visuals and sensoring information, Blue eyes technology makes a computer to sense and understand human feelings and their behavior and enables the computer to respond according to the sensed emotional level. The main aim of blue eyes technology is to give human abilities or power to a computer, so that the machine can naturally interact with human beings as humans interact with each other. The proposed methodologies in this paper detect human emotions are emotional mouse and emotion recognition by human eye expressions. Emotion mouse is an input device which is designed to track the emotions of a user by a simple touch of it. The emotion mouse is used to evaluate and identify the user’s emotions such as happy, sad, anger, fear, disgust, surprised, etc. when the user is interacting with computer. Human’s emotion recognition is an important component for efficient man-machine interaction. It plays a critical role in communication by allowing people to express oneself beyond the verbal domain. Analysis of emotions from human eye expression involves the detection and categorization of various human emotions or different state of mind. For example, in security and surveillance, they can predict the offender or criminal’s behavior by analyzing the images of their face from the frames of the video sequence. The analysis of human emotions can be applied in a variety of application domains, such as video surveillance and human – computer interaction systems. In some cases, the results of such analysis can be applied to identify and categorize the various human emotions automatically from the videos. II. METHODOLOGY USED A. Emotion Recognition From Human Eyes Facial expressions play an essential role in communications in social interactions with other human beings which delivers information about their emotions. The most crucial feature of human interaction that grants naturalism to the process is ourability to infer the emotional states of others. Our goal is to categorize the different human emotions from their eye expressions. The proposed system presents a human emotion recognition system that analyzes the human eye region from video sequences. From the frames of the video stream the human eyes can be extracted using the well-known canny edge operator and classified using a non – linear Support Vector machine (SVM) classifier. Finally, a standard learning tool is used, Hidden Markov Model (HMM) for recognizing the emotions from the human eye expressions. Human emotion recognition is an important component for efficient human – computer interaction. It plays a critical role in communication, allowing people to express themselves beyond the verbal domain. Analysis of emotions from human eye expression involves the detection and categorization of various human emotions and state of mind. The analysis of human emotions can be applied in a variety of application domains, such as video surveillance and human – computer interaction systems. In some cases, the results of such analysis can be applied to identify and categorize the various human emotions automatically from the videos. The six primary or main types of emotions are shown in Fig. 1: surprised, sad, happy, anger, fear, disgust. Our method is to use the feature extraction technique to extract the eyes, support vector machine (SVM) classifier and a HMM to build a human emotion recognition system. The methodology of emotion recognition from human eye expression is shown in Fig. 2. In this methodology image of the user sitting in front of the camera is captured. Then image representing a set of frames is preprocessed and a noise free image is obtained. The noise free image is edge detected using Canny Edge Operator. Using the feature extraction process, the eye regions are extracted from the resultant edge detected image. The extracted eye regions are classified using SVM classifier. Finally, the corresponding emotions are recognized. B. Emotion Mouse One proposed, non-invasive method for gaining user information through touch is via a computer input device, the mouse. This then allows the user to relate the cardiac rhythm, the body temperature and other physiological attributes with the mood. The block diagram of emotion mouse is shown in Fig. 3, this device can measure heart rate and temperature and matches them with six emotional states: happiness,surprise, anger, fear, sadness and disgust. The mouse includes a set of sensors, including infrared detectors and temperature-sensitive chips. These components can also be crafted into other commonly used items such as the office chair, the steering wheel, the keyboard and the phone handle. Integrating the system into the steering wheel, for instance, could allow an alert to be sounded when a driver becomes drowsy. Heart rate is taken by IR on the thumb and temperature is taken using a thermistor chip. These values are input into a series of discriminate function analyses and correlated to an emotional state. Specifically, for the mouse, discriminate function analysis is used in accordance with basic principles to determine a baseline relationship, that is, the relationship between each set of calibration physiological signals and the associated emotion. I. SYSTEM MODEL In this system, two methodologies namely emotion mouse and emotion recognition from eye expression are used. Emotion mouse will consider the physiological as well as biological parameters such as cardiac rhythm and body temperature, whereas on the other side emotion recognition from human eye expression considers facial expression for the detection of human emotion and mood. Fig. 4 shows the block diagram of the system. In this system the data from the heartbeat sensor and temperature sensor of the emotion mouse is given to the microcontroller. The output of the microcontroller is then fed to the computer. The value of heartbeat sensor and temperature sensor is compared with the standard range of each emotion and the suitable emotion is selected on the other hand a webcam is connected with the computer which will take the image of the person from a video sequence and will further recognize the emotion by detecting the eye part. The captured eye section will be compared to the images stored in database to detect mood of the person. After detecting the mood, the musicor audio command is played according to the detected mood. I. RESULT In proposed system, there are two results of the mentioned methodologies. Firstly, different eye expressions of the different people are taken in consideration by edge detection of eyes. Further each eye expression is categorized into a given set of emotions (happy, sad, fear, surprised, disgust, anger} to take in account a single standard expression for each emotion. Thus emotion of a person can be detected by comparing the eye expression of the person with the standard eye expressions of each emotion. Secondly, the values of heartbeat sensor and temperature sensor are compared with the standard value range of each emotion and accordingly the value range of a emotion that matches with the data values of the user is considered as the emotional state of the user. According to the detected emotion the music or audio command is played. II. CONCLUSION Recent research documents tell that the understanding and recognition of emotional expressions plays a very important role in the maintenance and development of social relationships. This paper gives an approach of creating computational machines that have perceptual and sensory ability like those of human beings which enables the computer to gather information about you through special techniques like facial expressions recognition and considering biological factors such as cardiac rhythm and body temperature. This makes it possible for computer and machines to detect the emotion of the human and respond to it.