Feasible Criminal Identification using Image Recognition

. Criminal identification using image recognition is a crucial task for law enforcement agencies. It involves identifying and tracking down criminals by analysing their facial features from images and videos. The system aims to improve the efficiency and accuracy of criminal identification while reducing human error. The system will be trained on large datasets of criminal images to ensure high accuracy and reliability. The paper will use publicly available datasets such as the Face Recognition Technology (FERET) and the Labelled Faces in the Wild (LFW) dataset to train and test the model. Metrics including accuracy, precision, recall, and F1-score will be used to assess the system's efficacy. The results and discussions will focus on the strengths and weaknesses of the model, and potential avenues for improvement. In conclusion, the proposed face recognition system shows promising results in identifying criminals and reducing the burden on law enforcement agencies. The manuscript discusses the objective of the paper, provides an overview of facial recognition technology, and summarizes existing approaches in criminal identification using image recognition.


Introduction
Biometrics, such as facial recognition, has gained prominence in verifying a person's identity by comparing their data to a database of known patterns.Facial recognition stands out as an advanced method due to its unique and revealing features.As technology advances, the accessibility of biometric scanners on consumer electronics has increased.The demand for high security and user-friendly experiences has led to the growing adoption of biometric authentication, replacing traditional verification methods.Facial recognition technology offers advantages such as intuitiveness, non-intrusiveness, and user-friendliness, making it valuable in various applications, including criminal identification.
Facial recognition technology has a rich history, with roots dating back to the mid-20th century.In the 1960s and 1970s, researchers began exploring techniques for facial analysis and recognition.Woodrow Wilson Bledsoe, often regarded as the pioneer of facial recognition, developed early methods for computer-based face recognition using geometric measurements of facial features.Today, facial recognition technology is widely employed in various domains, including law enforcement, security systems, mobile devices, and social media platforms.The proposed criminal identification system works based on the principle of facial recognition.It employs a multi-step process to identify individuals from facial images or video frames, in order to generate sustainable criminal lists.The working principle involves the following steps: • Face Detection: The system utilizes face detection algorithms to locate and extract facial regions from input images or video frames.This step is essential to isolate and focus on the facial features for subsequent analysis.• Feature Extraction: Once the face is detected, the system extracts key facial features from the facial region.These features may include the shape of the eyes, nose, mouth, and other discriminative landmarks.• Feature Representation: The extracted facial features are transformed into a compact and representative format known as feature vectors.These vectors encode the essential information about the facial features and serve as the basis for comparison and identification.
The challenges and pitfalls regarding the proposed work are as follows: • Privacy Concerns: Facial recognition raises significant privacy concerns due to the potential for mass surveillance and unauthorized tracking of individuals' movements.• Biases and Inaccuracies: Facial recognition systems may exhibit biases and inaccuracies, especially when applied to diverse populations or in varying lighting conditions, leading to false positives or negatives.• Ethical Considerations: Facial recognition technology poses ethical dilemmas, such as potential misuse for surveillance purposes, infringing on individuals' rights to privacy and civil liberties.• Vulnerability to Spoofing: Facial recognition systems can be vulnerable to spoofing attacks, where individuals can deceive the system using forged or manipulated facial images.

Existing methods
The criminal identification system using image recognition focuses on developing an efficient and accurate facial recognition system for criminal identification.The system will be trained on large datasets of criminal images, ensuring high accuracy and reliability.
Publicly available datasets such as the Face Recognition Technology (FERET) and the Labelled Faces in the Wild (LFW) dataset will be utilized for training and testing.The system's effectiveness will be evaluated using metrics such as accuracy, precision, recall, and F1-score.By achieving these objectives, the paper aims to contribute to public safety, enhance law enforcement efforts, minimize errors in identification, streamline the identification process, and facilitate more efficient and effective criminal investigations.Authors [10] highlighted the significance of ML in prediction, pattern recognition and error reduction across diverse fields, emphasizing the impact of AI in broad domain.Author [11] presented text classification algorithms for various applications and explores the use of machine learning in detecting phishing attacks.Image restoration is to enhance images by removing noise and restoring them to their original quality.The present approach explored various methods in both frequency and spatial domains, followed by analysing their performance using simulations [12].Authors [13] discussed the use of machine learning and neural networks, especially CNN, for recognizing handwriting patterns, with a focus on Telugu film industry names, achieving high accuracy (98.3%).Authors [14] emphasized the significance of feature selection in classification for accuracy and efficiency.It investigates combining features from different methods, demonstrating improved precision, contingent on dataset, algorithm, and metrics used.
Table 1.Summary of the existing approaches.

Problem statement
The problem is the need for an accurate and efficient facial recognition system for criminal identification that overcomes limitations in traditional methods.Current approaches, such as relying on human memory or physical identification documents, are time-consuming and prone to errors.There is a demand for a robust and automated system that streamlines the identification process, provides real-time results, and addresses privacy and ethical concerns.The system should improve accuracy, reduce human errors, and mitigate biases in identification results.

Objective
• Improving public safety: To improve public safety by accurately identifying criminals and preventing them from committing further crimes.• Enhancing law enforcement: To enhance the effectiveness of law enforcement agencies by providing them with a tool that can quickly and accurately identify criminals.• Streamlining the identification process: To streamline the identification process, reducing the time and resources required to identify criminals.• Facilitating investigations: To help investigators quickly identify suspects in criminal investigations, allowing them to focus their efforts on those who are most likely to have committed the crime.

Proposed method
The proposed criminal identification system aims to enhance the effectiveness and efficiency of law enforcement agencies in identifying and apprehending criminals.By leveraging facial recognition technology, the system offers a reliable and automated method for criminal identification, minimizing reliance on subjective human memory and traditional identification methods.The system operates by detecting and extracting facial features from images or video frames, followed by feature extraction and representation.The stored feature vectors in the database are then compared with the query image or video frame using matching algorithms, leading to the identification of the individual or a ranked list of potential matches.To ensure the accuracy and reliability of the system, a comprehensive dataset of criminal images is utilized for training the facial recognition model.The system can be utilized in real-time surveillance scenarios or investigations, aiding investigators in identifying suspects and allocating resources effectively.
The process that must be followed to develop a facial recognition system.Acquiring several photographs of different individuals allows one to begin building a face database.The shot should be taken from the waist up, with the subject's face towards the camera.A digital camera's acquired picture will undergo processing throughout the identity verification procedure.After detection and extraction, the picture will be prepared for processing.All of these images are then analyzed using OpenCV methods to extract the Eigenface as the baseline or common characteristic of human faces.These characteristics will be employed in the recognition phase, during which the system will look for a matching picture in its database.The image's identity will be confirmed if a match is found; otherwise, the process will end.

Modules and its description
The Login/Register module allows users to access the system.Existing users can log in by providing their username and password, while new users must register by providing their name, email address, and password.The registration process creates a unique user account for new users to access the system.In the Registration Stage, criminal data is recorded and stored in the system.This module captures information about the criminal, including a unique criminal ID, name, email address, phone number, and residential address, date of birth (DOB), age, gender, and details of the crime they have committed.Additionally, a photograph of the criminal's face is captured and associated with their profile in the system.
The Criminal Identification module is responsible for identifying criminals based on their facial features.To find a criminal from the existing database, the user navigates to the User dashboard and selects the "Find the Face" option.The software then conducts a search analysis by comparing the uploaded image with the stored images in the criminal database.
Various techniques such as Surveillance Footage Analysis, Automated Facial Recognition, Sketch Recognition, Object Recognition, and Tattoo Recognition may be utilized to identify similarities between the uploaded image and the images in the database.If a match is found, the system retrieves all the information associated with that criminal, including their criminal ID, name, email address, phone number, residential address, DOB, age, gender, and details of the crime they have committed.If there is no match in the database, the user may be prompted to register the criminal by entering all the required details.
The Results Phase occurs when a criminal face is successfully identified from the database.If the uploaded picture matches an entry in the criminal database, the system displays all the details related to that particular criminal.The user can view information such as their criminal ID, name, email address, phone number, residential address, DOB, age, gender, and details of the crime they have committed.Additionally, the user may have the option to add further details or update the criminal's information if necessary.This phase provides the user with comprehensive information about the identified criminal, assisting law enforcement agencies in their investigations and decision-making processes.By implementing these modules and functionalities, the system aims to provide a userfriendly and efficient platform for criminal identification, registration, and retrieval of criminal information.

Experimental results and discussions
Admin Login is used for admin to Login in the System without email address and password admin or any other person cannot login using this module.User registration where admin get all the information like total number of criminals available in the system and also navigate to other modules like existing/new, basic info of criminals, changing phase or showing result.Admin can view details of the users.He can perform operations like edit users or delete user and add the user in the criminal database system.Users can add new criminals and file basic information about the criminal such as type of crime committed any past records, create an id also.
User can add new criminal in the system with the unique criminal id and also find a criminal from the existing records that are already present in the database and if the admin wishes he could simply logout either.Here the user finds the criminal by uploading a direct picture of the criminal.Once he uploads the picture, the picture runs through the database and gives result.
Here the user can view all the criminals and gets information about them including the crimes they have committed.After running the find face algorithm if the face matches to any of the criminal present in the existing records then all the information about the criminal will be displayed including the criminal id, name, date of birth, address, phone number and past committed crimes.

Significance of proposed method
The proposed methods for criminal identification with image recognition have significant significance in the field of law enforcement and public safety.Here are some key points highlighting their significance • Enhanced Crime Detection Image recognition algorithms can analyze vast amounts of visual data, such as surveillance footage or crime scene images, and quickly identify potential criminals.• Improved Accuracy and Reliability Machine learning algorithms used in image recognition can be trained on large datasets, allowing them to learn complex patterns and features that may not be easily detectable by human observers.This can result in higher accuracy and reliability in identifying criminals, reducing the chances of false identifications and wrongful arrests.

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Real-time Monitoring and Alerts Image recognition systems can be integrated with live video feeds from surveillance cameras to provide real-time monitoring and alerts.Public Safety and Deterrence.The deployment of image recognition systems for criminal identification acts as a deterrent to potential offenders.Overall, the proposed methods for criminal identification with image recognition have significant significance in improving the effectiveness, efficiency, and accuracy of law enforcement efforts, ultimately contributing to the maintenance of law and order in society Overall, the proposed methods for criminal identification with image recognition have significance in improving the effectiveness, efficiency, and accuracy of law enforcement efforts, ultimately contributing to the maintenance of law and order in society.Multi-Modal Biometrics Integration: Integrating facial recognition with other biometric modalities, such as fingerprint recognition or iris scanning, ensures sustainable crime detection system.Multi-modal biometric systems can provide more comprehensive and accurate identification results.

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Conclusion and future enhancementThe proposed method for criminal identification with image recognition have significant significance in the field of law enforcement and public safety.Here are some key points highlighting their significance, (a) Enhanced Crime Detection, (b) Improved Accuracy and Reliability, (c) Real-time Monitoring and Alerts, and (d) Public Safety and Deterrence.