A SYSTEM FOR DETECTING MISSING INDIVIDUAL

Abstract

ABSTRACT A SYSTEM FOR DETECTING MISSING INDIVIDUAL The present invention provides a system to be used for detecting missing persons through a networked appliance comprised of devices that will include cameras, smartwatches, and scanning devices, operatively arranged to capture and process real-time facial data in any setting. Included within the system is a central database which comprises profiles of missing persons with the inclusion of allowing cross-checks to be made of captured facial data for possible matches. Coupled with the database, a processing unit employs face recognition algorithms to analyze the captured images and confirm the matches. If it does find one, then it alerts the authorized persons on all possible channels through visual, audio, or even digital means such as a mobile app, email, or any connected display units. The system also has a geolocation module to track the whereabouts of every point of capture; this enables fast location and recovery of the missing person. The encrypting mechanism keeps the data private and secure to ensure at all times its compliance with the relevant data protection regulations. The system also gives real-time updates and synchronization with public and private databases. Fig 1

Specification

Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See Section 10; rule 13)

TITLE OF THE INVENTION
A SYSTEM FOR DETECTING MISSING INDIVIDUAL


APPLICANT
K.RAMAKRISHNAN COLLEGE OF ENGINEERING
NH-45, Samayapuram,
Trichy, Tamilnadu, India- 621112


The following specification particularly describes the invention and the manner in which it is to be performed.
A SYSTEM FOR DETECTING MISSING INDIVIDUAL
TECHNICAL FIELD
The current invention comes under general public safety and identification systems. More directly, the invention pertains to a multi-device face recognition technology that would help in real-time identification of missing persons or people of interest in public spaces. The system would obviously strive to make the identification processes much easier, mainly by having highly advanced image recognition algorithms installed within cameras, wearable technology, and mall scanners, thus allowing law enforcement and security officers access to rapid and efficient identification.
BACKGROUND
Security and public safety are the very basis of modern society, something that is quite precious in the rapidly growing world. Although technological advancement has updated various spheres and sectors, the effective identification of missing or wanted people in real time still remains an arduous task. Even though public safety is being progressed, most of the traditional methods are mainly found on manual processing or single-point surveillance systems that eventually fail in such dynamic, high-traffic environments.
There is a significant need for efficient, high-volume identification systems that would be very useful in busy public areas like shopping malls, transportation hubs, or event venues-the real time insight that the authorities will require to identify and locate people of interest likely not being available. Current approaches are too slow and not suitable for the scalability required to monitor this kind of volume in real time. It typically results in delayed or missed identifications, therefore impairing the chances of security personnel to quickly react to potential critical situations.
In addition, with urbanization growing incessantly across the globe, there is a strong need for flexible and multi-device solutions that can easily be absorbed by public environments. Such a solution opens up the possibility of tapping the advances of face recognition technology while catering to the changing needs of an application area. Current systems are, however, almost entirely dependent on stationary or high-resource setups that are neither flexible nor ubiquitous across different types of devices.
This invention addresses this problem by providing a real-time solution across all kinds of mobile and fixed devices, empowering the security personnel, and eliminating reliance on standalone systems. Innovative face recognition technology integrated into cameras, smartwatches, and mall scanners will help create a seamless network for rapid identification and response, helping break the existing limitations of public safety protocols.
OBJECTIVE OF THE INVENTION
We will discuss this invention, whose primary purpose is to create a face recognition system capable of quickly identifying missing persons and persons of interest, hence making public spaces safer and allowing security teams to respond much faster.
Its main idea is to make that technology available on as many devices as possible-for example, cameras, smartwatches, and public scanners-for it to work in different environments without large, fixed setups.
Other motivation is the reduction of reliance on traditional surveillance. Since it's flexible to run on many mobile as well as fixed devices it's much easier to monitor large areas and crowded places in real time.
It also seeks to help the security forces by providing the instant and correct output which allows them to take immediate action in case any match occurs, for missing persons and for those marked for public safety purposes.
The other goal is to safeguard personal privacy, as data is to be used responsibly and securely - only for the purpose of identification.
The invention is intended to secure public spaces by reducing the time it takes to locate people in need or draw attention to them, therefore giving the community more peace of mind.
SUMMARY
The various embodiments of the present invention disclose a multi-perspective face recognition system designed to identify missing persons and persons of interest to increase public safety as well as to streamline identification processes in real-time. The face recognition system has been built across a network of devices: cameras, wearable technology, and scanning systems in public places-each connected to supply the image capture and identification capability when needed.
The system comprises of multiple devices such as cameras and scanners in mobile designs to capture high-quality images. Connected to a secure cloud database for all computations, they quite powerfully support fast and accurate matches in identity. The system allows for the rapid cross-referencing of facial images with alerts sent to security personnel in case an indication of match has been confirmed.
The adaptable device integration allows the system to perform well across environments and needs no large, fixed installations. Each device is linked to a central server that processes all data and security protocols to ensure private data on safe, electronic transmission of identification information.
All match results will appear on one user interface; the security personnel can easily access and control. The user interface is customizable, allowing the operators to place priority on specific kinds of alerts such as missing persons or people marked for an immediate response.
The system also includes wearable technology for live tracking to enhance the ability to locate identified persons within crowded or high-traffic areas. With this feature, live updates of movements by identified persons enable security teams to respond effectively and precisely.
All of these, and other features of the embodiments of the invention herein, will be better appreciated and understood by reference to the following detailed description of the embodiments of the invention and the appended drawings. Of particular importance are the following detailed descriptions of specific embodiments and elements, wherein it should be noted that modifications and adjustments to such specific embodiments and elements are possible without departing from the intended purpose thereof, with all such variations included within the scope of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
The design and functioning of the system will emerge clearly in the following description and figures:
Fig. 1 Flow chart showing the missing persons detection system. Fig. 1 portrays the whole process, from capturing images in public places by cameras and smart watches, to alerting competent authorities if a match is found with the missing person.
Fig. 2. Flowchart of face recognition. Each step of the process is presented, from taking and processing an image to comparing facial features against a database of missing persons and alerting authorities of potential matches.
Each feature of the drawings can be used separately or combined with any other features as necessary to the particular practical use.
Reference numerals employed in the description and drawings:
50 - Missing people detection system, 1 - Camera module, 2 - Wearable device (smart watch, scanner), 3 - Image processing module, 4 - Face detection module, 5 - Feature extraction unit, 6 - Central Database, 7 - Secure server and cloud storage, 8 - Tracking module, 9 - Alert Interface, 10 - Live display screen, 11 - Security notification system, 12 - Authorities (police, others) & 13 - Log entry system.
DETAILED DESCRIPTION
The invention refers to a self-contained system designed to locate missing persons and persons of interest. It is meant to harness the power of facial recognition technology into existing surveillance networks in a manner that proves both cost-effective and adaptable. The system uses strategically placed high-resolution cameras in public places, such as shopping malls and high-traffic locations, which include airports, to capture video footage of people and others moving through those environments. The video streams are then processed through sophisticated software that can detect faces and match the faces against a database of known individuals, including missing or wanted persons. The system is highly integrated with the pre-existing infrastructures so that the system can be deployed with minimal disruption and maximum efficiency, thus saving the time and costs otherwise incurred by establishing new surveillance equipment.
The central unit of the system accommodates the powerful processing capabilities required to handle the constant flow of video data. Each of the cameras has high definition sensors, enabling the capture of clear images under most conditions, including the low lights and overpopulated areas. Once the video is captured, it is forwarded to the central processing system, where advanced algorithms begin the painstaking task of analyzing footage in real time. This processing is critical because it allows one to identify faces immediately they are detected in the video feed, thereby giving security personnel the tool to act faster where there could be potential matches of the pictures against the database of missing persons or wanted individuals.
Behind it stands the facial recognition technology, itself a cutting-edge algorithm that uses deep learning and machine learning models to look up individuals through the features of their faces. Unlike traditional security systems, which merely record video footage, this system is proactive; it analyzes every frame of video footage and compares the people against the database that gets constantly updated. The facial recognition system is designed to be highly accurate in recognizing people, even in degraded conditions such as out-of-focus faces, partially obscured faces, or crowdsied environments. This is achieved by abstracting distinctive features from the face, including placement of eyes, contours of the nose, and overall shape of the face. Such features can then be used in the formation of the biometric template, which can then be compared with templates stored in the database.
This increases the accuracy of the system due to a rise in facial data over time and subsequently being able to improve on identification capabilities. It uses CNNs, which have been very effective in handling image-based recognition tasks. The training is done with extremely large data sets of thousands, if not millions, of facial images in which the system learns subtle differences in human faces that are perhaps impossible to intuit. CNNs can work their way through multiple layers of pixel data from images to gradually build up a recognition model that can match faces with very high precision. This training process is incessant, and the system will continually improve its ability to recognize faces as it exposes itself to new faces to ensure efficiency when more faces are included in the database.
The system will immediately alert once it attains a match of the face in the video feed with one in the database. An alert is sent to the security personnel with all details of the person involved-full name, recent photograph, and all other information that may be relevant. The security team can then promptly respond whether it is by apprehending the suspect or by reporting the missing person to the appropriate authorities. This capability in real time is absolutely vital in places where speed is of paramount importance, such as crowded events or high-security areas. Security personnel can act much faster than through the traditional techniques, which may involve identification for hours or even days.
It is correct but flexible. It can easily be incorporated into existing security infrastructure so that it can actually operate in concert with other surveillance systems and devices already on hand. It can make use of any cameras that feed video data into the central processing unit, be it from a given brand or model, and can easily be expanded to cover more areas or environments when necessary. This is a scalability feature, as the system can be so designed that, depending upon whether it is a small store, huge shopping mall, or an entire city block, it can be tailored to almost any location. The central processing unit can process large volumes of data and can process video feeds coming from multiple cameras simultaneously without losing much in the way of speed or accuracy.
One of the most important considerations in the design of this system is security over the data that it is handling. All the biometric templates and personal information that are stored in a protected database with encryption over it to safeguard against unauthorized access to it. Security personnel accessing the database for updating or modifying records authenticate themselves via secure login protocols. The system has been constructed keeping the basics of privacy in mind, satisfying all the laws and regulations placed for data protection. It limits people to accessing sensitive data, thus offering no invasion of privacy at any time. It deletes outdated or irrelevant data after a specified time in order to maintain an up-to-date database without keeping unwanted records.
It can be implemented in almost any setting-from private enterprises to government buildings, public spaces, and transportation hubs like airports and train stations. This flexibility is one of the key strengths of the system-variety allows it to be used in any number of applications. It can easily be integrated with other existing surveillance setups; thus, it is easy to deploy in locations having already established security infrastructures. The system can easily be mounted and configured without making big changes to the environment, hence making it ideal for facilities that need to quickly implement advanced security measures.
Real-time monitoring is another main feature of the system. It analyzes the feeds of videos continuously, each frame being processed to detect faces, thus making it possible to make instant identification. The system is capable of monitoring areas in real time; this feature proves significant in any situation requiring rapid action, such as spotting a missing individual in the crowd or apprehending a suspect in a high-security environment. This real-time aspect ensures the security teams are always in the know for updates on the status of their surveillance operations.
It is designed with efficiency in mind concerning communication. Alerts are sent directly to the security personnel via a dedicated app or software interface the instant they occur. This is synchronizing different teams and departments such that everybody involved in the security operation is on the same page. Thus, the system can be adapted to the specific needs of an organization, so that different types of alerts are ranked with different levels of priority: a match with a missing person might trigger one set of action, whereas a wanted criminal should trigger another.
The system also entails high scalability; thus, it is easily deployable in both small and large environments. The demand for more surveillance coverage is increased by the system since it can be expanded using more cameras and processing units. The scalability of this system ensures that it will keep performing efficiently even as the numbers monitored areas increase.
An automation system with real-time identification and alert capabilities goes a long way in helping security teams respond to emergencies and threats. The system not only makes security operations more efficient but also contributes towards better safety and security of public areas by incubating incidents such as missing persons or wanted criminals much earlier.
The design of the system allows it to connect with outside databases. It can connect into government databases holding information about missing persons or wanted criminals, thus producing a match right away if a person is identified. This functionality ensures that the system can be easily deployed in all manner of law enforcement agencies, from the public to private sectors, hence beneficial both for security purposes and public safety measures. To be more specific, the system can be set to aggregate data from however many sources necessary. It contains an updating mechanism in real time based on the database of persons of interest, ensuring constant availability of the most current and accurate information in use.
In addition to facial recognition, the system uses advanced analytics in order to strengthen its capabilities for detection. For example, the system detects suspicious behavior patterns, for example, a loiterer or someone acting suspiciously. Such technology in pattern recognition contributes to an additional layer of security, and the ability of the system to detect people and what these individuals are doing allows for further security personnel's ability to anticipate some threat. These algorithms are trained on massive datasets that capture the widest range of scenarios and behaviors, thus enabling the system to learn adapting to all situations and to recognize a wide variety of potential risks.
These machine learning techniques introduce a factor where the performance of the system improves over time. The system learns from its mistakes as it processes more data, changing and tuning up its recognition models to minimize false positives and false negatives. Additionally, the system uses self-learning algorithms whereby it can boost its ability to match faces as well as identify unusual patterns without requiring constant manual adjustment. This is very helpful in a dynamic scenario with changing conditions where the environment might frequently change, say in a mall that is full or during a crowded event.
Its designing allows the collected data to be processed in an ethical manner and respecting confidentiality. The personal information, therefore, pertaining to images or biometric data are stored in a secure manner and can only be accessed by the required personnel for valid reasons. The data retention policy ensures that there is only retention of information for as long as it is still required and then it is deleted in accordance with data protection laws. This acts to reduce the misuse risk of data but still becomes a very effective tool for public safety and security.
Optional features that can be added to the system include automatic camera pan-and-tilt controls, making the cameras follow a moving person/individual as they continue through the monitored area. This type of dynamic camera control is enabled by the advanced tracking algorithms the system has that might predict where individuals are likely to be based on their trajectory. The system ensures the ability to maintain a clear view of the individual through real-time adjustment of the camera position, even in the case of crowded or constantly changing environments. This feature ensures enhanced facial recognition accuracy through continuous tracking of people without the loss of detail in such information as facial expressions or identifying marks.
The greatest benefit of this system, however, remains the insight that data analytics provides from it. Since the system analyzes video footage and detects faces, it generates useful information regarding people movement patterns, frequency of events, and behavior as a whole within the area being monitored. Analysis of the collected data can be done to understand customer behavior, crowd dynamics, or security trends so that stakeholders may make decisions wisely in the future. For instance, the retailers will be able to change store layouts according to specific patterns through which the shoppers move in stores, while security teams will be in a better position to pinpoint areas that pose increased security risk or catch emerging trends in criminal behavior.
The system deployment model is flexible and can be made appropriate for organizational needs. It can be used in a standalone configuration for smaller venues or it can be configured as part of large, interconnected networks for larger and more complex environments. The system can be configured in large, multi-site networks to share information across locations, so one security department can actually access real-time information from a different site. This way of networking brings security teams a larger scope of information. On this basis, they can make stronger decisions about probable threats.
The system further proves to be an important accountability layer in the form of keeping detailed logs of all actions undertaken by the system. The logs include information relating to such matches identified, alerts sent, and further action undertaken by security personnel following the alert. The logs can be checked at any time to make sure the system works and to audit the use of this system. Such logs are quite useful in circumstances where there may be questions about the reliability or authenticity of an identification.
Most importantly, the system reduces disturbances in everyday life in the observed area. This offers a strong security measure without intrusiveness or interfering in particular ways with everyday activities. The cameras were placed in such a way that they completely integrate with the environment and, in the background, there's the processing system without its interference being detected. This will ensure the running of the system with minimal exposure and without unnecessary attention focused on it, for in an environment such as a mall or an airport, privacy and convenience are the highest-sought-after issues.
Additionally, the alerting capabilities of this system can be customized with regard to urgency level. For instance, a match with missing person may open a low-priority alert forwarded to security personnel for further research, whereas a wanted criminal may open up a high-priority alert along with an appropriate set of activities. Through such customizing, the nature of the alert builds up the appropriateness of the response and sets up priorities by the security team about the situation they are dealing with.
The whole security structure is much more enhanced with the overall system since the infrastructure is greatly improved when there is an efficient, flexible, and scalable solution for identifying missing persons and wanted individuals. It offers a proactive approach to public safety so that the authorities and security teams can be more effective in their actions at any given moment-mostly time. With facial recognition technology and machine learning real-time alert capabilities and data analytics, the system can withstand today's intensified surveillance environments, placing it in a very convenient position for businesses, government agencies, and law enforcement.
Finally, integration with existing infrastructures along with its improved security operations enhances and performs real-time data analytics, which further supports its high efficiency toward public safety and security needs. Not only does it enhance the speed and accuracy of person identification, but it also leads valuable insights toward optimizing various security measures in place. This will ensure safety both for individuals and communities for a long time into the future as technology continues to advance.
The system can even be integrated with various devices and sensors to enhance its effectiveness. For example, it could be integrated with other types of surveillance such as thermal cameras or motion detectors, or even wearable devices. These auxiliary tools offer other layers of monitoring, so the system is perfectly capable of functioning accordingly in different settings-whether in dimly lit environments or highly populated areas with much movement. A network of complementary devices can be used to cover more ground and, thus, to provide a much better situation analysis. Using such a multi-sensor approach does not only make the system reliable but also ensures that face detection accuracy is maintained to be high in complex or challenging environments.
Probably, the most important aspect of the design of the system would be its adaptability to various environments. Whether to be deployed in a busy shopping mall, Airport, train station, or public event, the system is flexible enough to meet the needs of each setting. For instance, in retail space, the system can be used to track customer behavior and get some products that identify potential shoplifters or those who have been flagged in the database for suspicious activities. These would also be applied to monitor employees or visitors in high-security places to prevent unauthorized access, hence protecting sensitive information or even sensitive assets.
The system can also analyze crowd dynamics in real-time, which is a very critical feature when holding large events such as concerts, protests, or sporting events so that public safety can be ensured. Such a system, by continuous tracking of individuals and detection of anomalous behavior patterns, may alarm security persons of potential threats such as overcrowding, fights, or suspicious behavior patterns by certain individuals. Programmed identification of movements of specific groups or individuals may also help in the easier monitoring of particular individuals in a crowd and intervention if necessary.
The system architecture is capable of deploying across many locations for scalability. A big city or cross-regional operation would best be set up with a central command center where data from various cameras and sensors is centralized and analyzed. The use of this central hub makes it possible to have easy coordination between different security teams and get a broader view of the security situation at all the sites involved. In such an architecture, the cloud infrastructure that the system is built on allows all of its backends to be updated and shared in real time. Then, it becomes easier for the security team to fetch any data from whatever location and respond to an incident as it happens.
The cloud architecture of the system also makes the latter scalable and efficient. By performing data processing on the cloud, the system can process huge volumes of footage without compromising on the speed or the accuracy of the system. It ensures this distributed processing model that even at the time of peak, when many data were going to be processed, the system would continue to remain efficient. Additionally, it provides for a high reliability mechanism since the processing loads are diversified into multiple servers, reducing the chances of downtime or system overload.
Having in mind data privacy and security as the essential aspect in the design of this system because of the very sensitive data collected. The system ensures strict data protection through stringent practices on the encryption of information both at rest and during transit. Industry encryption protocols adopted protect integrity for data regarding unauthorized access. Appropriate control measures on access are implemented through multi-factor authentication and role-based access control, which limit access to interactions with sensitive information.
It is also designed with transparency and accountability. Every activity that the system performs, from the identification of a face to corresponding action, is logged for information on detail. Such logs may always be consulted to confirm the accuracy of the actions of the system and determine if it acts as it should. This level of accountability will be important in high-stakes environments where decisions based on the system's data could have ramifications. The system also has a complete audit trail that tracks all aspects of user access and changes made to the system to ensure full transparency in its operation.
In terms of user experience, the system is designed to be intuitive and easy to use. This system will be accessible to security personnel and authorized users through an intuitive interface that assists in the navigation of quickly-accessed relevant data. The interface gives a clear view of the area of surveillance, with real-time footage from available cameras and alerts or notifications. It makes it easy to identify persons of interest by searching through individuals based on name, appearance, or behavioral patterns. Even the system gives visual and audio cues to remind of significant events, thus giving ample time for users to respond appropriately in due course of emerging situations.
Besides real-time monitoring and alert systems, the reporting feature by the system is also completed whereby it can pull out comprehensive reports on security cases, like missing people, absconded criminals, or abnormal behavior. These reports may be made to include certain data points, such as time, location, and the actions performed by the security personnel. The reports can also be exported in many different formats, which makes it simple to share the data with stakeholders or analyze them further. This reporting feature makes it very helpful for law enforcement, businesses, and other organizations who need to keep records of security activities in great detail for legal or regulatory purposes.
The ability of the system to scale up as well as the maintenance and Updates is also part of the concept. It is easy to update with features, security patches, and improvements at regular intervals. Whenever new advances in facial recognition technology, machine learning algorithms, and data analytics are available, the system can be updated to benefit from these new innovations. This will ensure that the system takes advantage of all the technological advancements and provides the most accurate and effective security solution possible. The updates are deployed with no interference on the operation of the system; thus, users can still use the newest features without having to install hardware.
The implications of the system in the security environment are absolutely far-reaching. With its ability to give a very accurate and real-time solution in identifying missing persons or wanted criminals in a scalable manner, this is indeed an advancement in surveillance technology. The tool can analyse volumes of data, spot faces very precisely, and trace suspicious behaviors in real time. This makes the tool empowering when enhancing public safety. Also, it will proactively be used in controlling security so that threats may be identified before they escalate into full-blown events that may harm people and communities.
As the technology keeps developing, it is apt to be integrated with other emerging technologies. These include artificial intelligence and the Internet of Things. AI would make the system more robust and smart because the decisions made by it would be perfect enough to detect patterns of behavior as complex as possible and predict them very accurately. The IoT would expand the coverage of the system with sensors in everyday artifacts such as wearables, automobiles, and smart home-related technologies. This convergence of technologies would open up possibilities for more panoramic and effective security in the future.
Public safety and security technology is led by the system. With advanced technologies like facial recognition, machine learning, real-time data analytics, and cloud computing, it offers a full-spectrum solution for missing persons, wanted criminals, and suspicious behavior. Its scalability, adaptability, and ease of use make it a very versatile tool that can be rolled out to almost any environment, from shopping centers to highly secure facilities. As the system is integrated with other advanced technologies and continues to evolve, it will surely be of great importance at a global level in raising public safety awareness and in protecting individuals.
, Claims:CLAIMS
WE CLAIM,
1. A system for detecting missing individual comprising:
a plurality of units consisting of cameras, smartwatches, and scanning devices appropriately arranged to capture and process facial data in real-time in several settings, wherein the elements essentially include an idea of maintaining a centralized database that contains information on missing persons and where the obtained facial data are to be cross-checked with already existing ones for finding any match;
a processing unit operatively connected to the database, and configured to process the captured facial images in which the processing unit applies the face recognition algorithms to confirm possible matches;
a alerting mechanism that raises signals in case of a match of any missing person's profiles found in the database, composed of visual, audio, and digital notifications through various available channels, which can include mobile applications, emails, or connected display units;
a module in wireless communication, integrated into the system, which will ensure distance access to the database and the real-time monitoring of captured data for viewing and validation by authorized personnel;
a module on the system devices for geolocation, designed to record precisely where each capture point has been located, thus making possible an attempt at tracking and to guide authorized personnel to the area in which the pictured individual is located;
a privacy and security module that encrypts all retrieved data and communication streams so as not to access data without authorization and breaches, in conformity with the provisions of the data protection rules; and,
an update and Synchronization, for periodical updating of the database containing the latest profiles of missing persons and for the time-wise upgradation of all elements of the system.
2. The system for detecting missing individual as in claim 1, wherein said processing unit is autonomous and therefore capable of operating singly and that can execute alarm protocols without a human when there is a match found in the collected data.
3. The system for detecting missing individual as in claim 1, wherein the alert feature would comprise an emergency contact feature that would automatically dial the police or other security agencies upon validation of a missing person.
4. The system for detecting missing individual as in claim 1, with a module bearing the geolocation devices from which information providing the responding team with real-time tracking can be obtained so that they may trace and locate the found person with quick direction and pace.
5. The system for detecting missing individual as in claim 1, wherein the wireless module is mounted to function compatibility with a cloud-based database encompassed by distributed data storage facilities, multi-devices, and location high-speed access.
6. The system for detecting missing individual as in claim 1, wherein the algorithm of the face recognition is an adaptive one that uses learning power of machines and its ability to change itself in order to improve its accuracy as well as the rate of recognition by learning previously identified patterns or features.
7. The system for detecting missing individual as in claim 1, wherein the privacy and security module is generally provided with a multi-factor authentication protocol whereby access to the system and modification in the database may require dual authentication so as to avoid unauthorized changes or misuse of data.
8. The system for detecting missing individual as in claim 1, wherein said update and synchronization feature equips with automatic synchronization of both public and private databases so that the latest data about missing persons is available for the purpose of identification.

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