Python
Aim:
To detect and recognize the face and we can differentiate between citizen and criminals and further investigate whether the identified person is criminal or not.
Abstract:
Face recognition can be considered one of the most successful biometric identification methods among several types of biometric identification including fingerprints, DNA, palm print, hand geometry, iris recognition and retina. Face recognition provides biometric identification that utilizes the uniqueness of faces for security purposes. The problem with face recognition using biometric identification is its lengthy process and the accuracy of the results. This paper proposes solutions for a faster face recognition process with accurate results. The proposed face recognition process was done using a Machine Learning. This improved face recognition approach was able to recognize multiple faces with high accuracy level.
Existing System:
The Existing System is single face recognition system and a new face detection approach using color base segmentation and morphological operations is presented. The algorithm uses color plane extraction, background subtraction, thresholding, morphological operations (such as erosion and dilation), filtering (to avoid false detection). Then particle analysis is done to detect only the face area in the image and not the other parts of the body. This method given result is poor performance and accuracy. So we will move the proposed system.
Problem Definition:
The main problem addressed in this research is the efficient identification of criminals using facial features. Traditional methods of criminal identification rely on manual processes and are often time-consuming and prone to errors. This research aims to overcome these limitations by employing computer vision techniques to automate face detection and recognition, enabling faster and more accurate criminal identification.
Proposed System:
The proposed system consists of 4 steps, including training of real time images, multiple face detection, comparison of trained real time images with images from the surveillance camera, result based on the comparison. In our proposed system, the video obtained from the camera will be converted into frames. When a face is detected in a frame, it is preprocessed where noise and redundancies are reduced. The processed real time image is compared with the processed images already stored in the database. If he is criminal/suspect, we get a details through firebase.
Advantages:
Automation: The proposed system automates the process of face detection and recognition, reducing manual effort and accelerating the criminal identification process. Computer vision techniques offer high accuracy in face detection and recognition, facilitating reliable identification of individuals involved in criminal activities. The automated system enables fast and efficient identification of criminals, aiding law enforcement agencies in their investigations and improving public safety.
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