Smart Receptionist – In the Entrance of every company, Reception is there to check the Visitor’s details and Employee details, and all other details of the company. That all are managed by one person he is called Receptionist. In this project, we are developing one system that Performs the work of a Receptionist in an automatic manner. Here we use Face recognition to find out the Person who visited the company. Open CV Algorithm is used to extract the facial features of humans. If the new person visited the company means his face is Stored in the Database. If the other old persons visited the company means This system book the Appointment for that person. Once the face is Recorded means it can identify whenever the Person visited the company. The System reduces human work and manpower. This system is Portable and Available at a low cost.
Computer-based face detection and recognition systems are rapidly spreading are various sectors such as malls, universities, and ministries. The goal of this research is to build a system that can detect and recognize the faces of people using image-processing techniques. Practically, this idea can be implemented in large places to provide security. The benefits of this system are:
- Use available computers (servers) in a corporation with microcomputers.
- Expand the desired microcomputer capabilities.
- Implement the face detection and recognition algorithms to run over the microcomputer to notice the effect of combining multiple computers with a microcontroller.
Using multiple servers three in this scenario- will allow a faster recognition for a stream of faces, instead of working among the same microcomputer to detect and recognize the stream of faces, which implies dividing the process in between the different electronics available to reduce the processing time for both detection and recognition.
In the Existing System, we are using a voice-based assistance system using Voice-based Alexa Module like a chat but we can interact with that module. It is placed in reception we give voice input it identifies the voice based on the input command it will react for example if we want appointment it will give voice output
This Proposed is Built with Raspberry pi 3 interfaced with a camera and speaker. A face recognition algorithm is used here to identify the person who visited the company. If the Person comes the First time means his face is recorded and stored in a database. If a known person comes means it identifies the face of the person it fixes the appointment for them. It gives the Voice output as appointment fixed.
BLOCK DIAGRAM DESCRIPTION
This system consists of Raspberry Pi ? 3 as a heart of a system that is Interfaced with an input Camera and Output Speaker. A person’s face is given as input to the camera by using the Open CV algorithm we can detect and identify the face. Once the face is matched with the database raspberry fixes the appointment of the respective Person. Otherwise, it stores that face in the database. If the person visited again means his face is detected.
Raspberry is running on Raspbian? Jessie which is a Linux-based operating System it can perform multi-tasking operations. Once the Program starts running means it will detect the face and identify the face is already present in the database or not. If it is there means it gives voice output as Your appointment is fixed through speak library in python. In other words, if a new person enters the organization, it will detect the face and store it in the database, and popup one GUI to enter the Name and time to fix the appointment. If the same user is again detected it will show the details about the appointment fixed for the person. Likewise, the system will maintain the database for each individual person.
- Raspberry pi
- Raspbian Jessie OS
- Haar cascade for facial recognition
- Espeak library for speech
A distributed system was designed to process images from capturing, to face recognition using one microcomputer and a number of supporting computers. The possibility of utilizing computers to support the microcomputer in face recognition would speed up processing, but the cost is to distribute the database on different machines, and the security between these machines should be considered. Increasing window size increased the error rate. When the scale factor increases, the average error rate increase because image size reduction will decrease the quality of the image and consequently increase the error rate. Also, more images for the same person will enhance the correlation rate. Results vary depending on lighting, distance, and the resolution of the camera.