Missing_People_Identification_DL

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System Architecture

We propose a web-based missing person identification system that utilizes face recognition to identify missing persons in images uploaded by users. The system is designed to be robust, accurate, and easy to use, and to protect the privacy of missing persons and their families.

1. A user can either upload an existing picture from their gallery or capture a new one using the device's camera.

2. The provided image will be analyzed using a Convolutional Neural Network (CNN) algorithm to detect the presence of a missing person's face.

3. If no face is detected, the user will be prompted to upload a different image.

4. Upon successful face detection, the face coordinates will be calculated using the Euclidean Distance Algorithm and stored in a database.

5. If the detected face coordinates match any registered face in the database, the user will be directed to an Informer Verification page.

6. Otherwise, a message will be displayed indicating no matches were found. Following verification, the police station will receive an email notification along with the informer's details.

7. After gathering the information which the local police station has gathered via mail. He / She verifies whether the missing persons identity already exists in their database.

8. If the identity matches their existing records in database. They will take the necessary action.

Face Recognition Modules

The system consists of four main modules:

Admin module: The admin module is the central hub for managing the system. Administrators can use the admin module to perform a variety of tasks, which includes:

• Add, modify, and delete users.

• View reports and analytics on system usage.

• Manage the database of missing persons.

• Configure the system settings.

An essential part of the system is the admin module, which gives administrators the ability to verify that the data is correct and current and that the system is operating efficiently.

User module: The interface that users utilize to communicate with the system is called the user module. Users can use the user module to perform a variety of tasks, which includes:

• Upload a photo of person they suspect is missing.

• Search the database for missing persons records.

• View information about missing persons.

• Inform the police about a suspected missing person.

The user module is designed to be easy to use and accessible to everyone, regardless of their technical expertise.

Searching module: The searching module is the engine that powers the system's search functionality. Users can use the searching module to search for missing persons in the database by name, age, last known location, or other criteria. The searching module returns a list of matching missing persons, along with their information.

The searching module is a critical component of the system, as it allows users to quickly and easily find missing persons.

Inform to police module: The inform to police module allows users to inform the police about a suspected missing person. When a user clicks the "Inform to police" button, with the user's location and the photo of the alleged missing person, the system notifies the closest police station.

The inform to police module is a valuable tool for helping to find missing persons quickly and safely. The system works as follows:

1. Users visit the website and upload a picture of a person they believe is missing.

2. The face recognition module compares the user's image to the images in the database.

3. If the face recognition module finds a match, the system returns the matched face in the output and provides the user with the following information:

   • Name of the missing person

   • Age of the missing person

   • Last known location of the missing person

4. The user can then choose to inform the police by clicking the "Inform to police" button.

This system has several advantages over traditional methods of missing person identification. First, it is much faster and more efficient. Second, it is more accurate, as it is less likely to be fooled by false positives. Third, it is more accessible, as it can be used by anyone with an internet connection. Fourth, it protects the privacy of missing persons and their families by not providing contact information for missing persons to finders.

System Requirements

The proposed deep learning-based face recognition system can be implemented on a variety of hardware and software platforms. To ensure optimal performance, the following minimum system requirements are recommended:

Hardware Requirements:

• Processor: 2.0 GHz or faster

• Memory: 2 GB RAM or more

• Storage: 10 GB of available hard disk space

Software Requirements:

• Operating System: Windows 7 or later, Android 9 or later

• Web Browser: Firefox, Chrome, Edge, Brave

Results

The proposed face recognition system was evaluated using a dataset of 200 images of missing persons and 100 images of non-missing persons. The system correctly detected faces in 98% of the images and matched them against the database of missing persons with 98% accuracy. The system also correctly identified missing persons in 80% of cases and informed the police about potential missing persons in 75% of cases. These results demonstrate the system's ability to accurately detect and identify missing persons from images.

The following screenshots illustrate the main features and functionalities of the proposed face recognition system:

1. Home page: It provides minimalistic search interface as shown in Fig 2. The home page dashboard provides a concise overview of missing persons cases. It includes counters for total, tracked, and found missing persons. It also displays gender breakdowns. Dashboard serves as a valuable tool for understanding the status of missing persons cases and identifying trends. Searching option through images:

• Upload an image file.

• Click on the "Search" button.

• The system will analyze the image and display similar images or information about the image.

2. Search by Clicked Picture: If the searched person's image matches the database, the reported image of the person will pop up for verification by the uploader as shown in Fig 3. Click on the image if it is correct. If the image is not found in the database, a "No matching image found" message will be displayed as shown in Fig 4. If image is not clear then it shows “No face detected in uploaded image” message will be displayed as shown in Fig 5.

3. Match found: If the searched person's image matches the database, their name, registration date and place, police station, and region of the incident will be displayed as shown in Fig 6. An "Inform Police" button will be available for further action.

4. List of all missing people: The Missing Persons Page offers a user-friendly interface for locating missing individuals and provides a comprehensive list of missing persons with their names, details button, and images as shown in Fig 7.

Conclusion

Deep learning-based face recognition has the potential to revolutionize missing person searches by providing a fast and accurate way to identify missing individuals in images. The main goal of this proposed system is to find missing people in an area where no camera surveillance is available using a web application in which common person can upload picture and inform to police about missing people if found. With this aim in mind, we used deep learning algorithm like CNN to achieve this proposed system with 98% accuracy.