Advancement in IOT based application has become the state-of-the art technology among the researcher due to the availability of Internet everywhere. To make the application more user friendly, web based and android based technologies have gained their importance in this cutting edge technology. In this paper, device with emotion detection enable automation using telegram app user can manage their home security. This system is very useful for reducing the cost of monitoring the movement from outside. In this paper, a real time recognition system is proposed which will handle images very quickly. The main purpose of this paper is to protect home, office by recognizing people. The proposed system is real-time, fast and has low computational cost.
Computer vision can present more security system in the IoT platform for smart houses. It has abilities to recognize a person in the incorrect area and at the wrong time because this person may be a malicious one for the environment. It has an assortment of large applications in the ranges: public security, access control, credit card verification, criminal identification, law enforcement commerce, information security, human computer intelligent interaction, and digital libraries. The face is the most important part of human’s body. So, it can reflect many emotions of a person. Long year ago, humans were using the non-living things like smart cards, plastic cards, PINS, tokens and keys for authentication, and to get grant access in restricted areas like ISRO, NASA and DRDO. The system will fall into two categories; face detection and face recognition. Facial recognition is a way of perceiving a human face through technology. A facial recognition system used to map facial features from a photograph or video. It compares the information with a database of faces. Facial recognition can help to verify unique identity of human face. Using face detection, we can get the information we need to perform tasks like exaggerate selfies and portraits. There are a few techniques for fetching the most important features to implement face recognition. HAAR-CASCADE, and Local Binary Patterns (LBP) along with feature extraction techniques and are used for classification of emotion for the said automation system. To get fast discriminatory performance and good results, these techniques are chosen for face recognition. OpenCV is the most widely used library for computer vision. It uses machine learning algorithms to search for faces within a picture. Patterns and features that must be matched. The algorithms break the task of identifying the face into smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called classifiers. In this paper, Raspberry Pi 3B+ is utilized and Raspberry Pi camera is connected to it. The system will take an image when ultrasonic sensor detects any movement. Then, computer vision is applied to the captured images. Subsequently, the system sends the images to a smartphone via the Internet.
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