Surveillance and security in traditional sense is now moving forward – in leaps and bounds. Gone are the days when people were using analog cameras. Analogs are rapidly being replaced by digital cameras which enable video analytics to perform on an incoming digital stream. Also, between 2005 and 2010, there was a massive push to standardize the interface between the camera, and the software that talks to it over an ethernet cable. ONVIF – the Open Network Video Interface Forum – meant to provide and promote standardized interfaces for effective interoperability of IP-based physical security products, laid these standards. (Though many camera manufacturers claim that they are ONVIF compliant – one must check onvif.org to confirm). That development disrupted the stranglehold of camera manufacturers with their partners and allowed many other players to enter into the market as proprietary protocols were no longer required. Most of the terabytes of stored video is useless as it does not carry any useful information. Manual searches need to be conducted to find the relevant information one is looking for. This turns out to be a time-consuming process, as well as by the time information is found it might be out of date. This is where video analytics comes in fray which helps to some degree by looking for only relevant information – thereby saving time and resources. (https://www.securitylinkindia.com/feature/2019/09/11/actionable-video-intelligence/). Even though video analytics saves a considerable amount of time, it still does not avoid the manual process involved in looking at video instead of data. Now at this point – artificial intelligence and machine learning are meant to come in. Artificial intelligence builds a model based on a few initial parameters that are input by the user. Without getting into the details, it quickly builds a neural network and tells the confidence level of each object found in the video frame. This is a highly mathematical process involving convolution, calculus, probability and statistics. Based on the confidence level of each object found in the frame, one can fine tune the neural network by changing the input parameters. This fine tuning is called Machine Learning by which the neural network raises the confidence levels above 95% for each object found. We have done this in our ‘intrudX’ product whereby object confidence levels go from as low as 60% to as high as 98%. One can now put the neural network in training mode telling it the target end result a user wants. The machine can self-learn by varying hundreds of input parameters till the end target is met, and at this stage the user gets the model what he has been expecting. He now continues to use this highly accurate model to build his applications to solve problems specific to their market vertical. So, what have AI/ ML done? They eliminated the requirement of video examination – now, only the extracted data from the video stream is examined. This is a far more intelligent way of examining video streams in a far more efficient way – allowing the end user to build multiple intelligent applications on top of this. This is the ‘wave’ of the future as multiple petabytes of data cannot be examined after this fact. With the number of cameras exponentially increasing all across the globe, the best way to process video is on the fly – in real time – as it saves time, money and resources across the board. However, some time and money have to be invested for a particular use case to fine tune the neural network model. Once this process and methodology are mastered, one can use it for other use cases as well. In our case, some of ‘intrudX’ models took up just 30 minutes to bring up the confidence level above 95%, while in other cases it has taken even a week. Factors that affect the training period are: Lighting, Number of objects in the frame, and Complexity of the shape of the object.     A couple of used cases are described hereunder to make this concept clear. USED CASE I Implementing standard operating procedure (SOP) While defining SOP for a drug testing methodology in a pharmaceutical laboratory – following are the requirements: Capturing and time stamping when an employee enters and exits the laboratory. Measuring the procedure when the drug testing starts. Identifying colored flasks and test tubes, and their movement from one step to the next. Identifying the microscopes and other medical instruments used in the measurement and how they are being used. Flagging the deviation, if any, from SOP and report it to the administrators. Obviously, one can use identifying objects in the video stream and determine whether the SOP is being followed or no. This can be used by the laboratory management team to improve overall efficiency of the laboratory and its’ employee performance without looking at the video streams. USED CASE II Measuring queue lengths While measuring queue lengths at bank counters, airport check-in lines, hospitals etc – requirements are as follows: Determine queue lengths to fix arrival and service rates. Queue lengths will increase if service time is greater than the person’s arrival time. Flag these so the service efficiency can be improved Summary The neural network model has over 25 million pre-defined objects in the database which have been developed using artificial intelligence techniques. In a typical end user case a very small subset of these 25 million predefined objects is required. New objects are continuously being added to the database. The model also allows itself to be put in training mode based on what the end user really wants.     ‘intrudX’ Object Classification Engine takes advantage of this feature and provides interfaces so that end user case applications can be developed rapidly and be put to use. We provide extracted data, interface to the ML neural network model, as well as application development services for the customer. By- Paresh Borkar, Founder & MD, intrudX Security Solutions, LLC  

An access control system (ACS) is an ultra-critical component in the chain of electronic security – however in India, even many large organizations do not give access control the credit that is due. This article enumerates ten must-have features that any modern access control system should possess. While designing security systems, we are especially attracted to projects which consist of access control systems (ACS) – because only the discerning customer considers access control as a ‘security requirement’ rather than a ‘time & attendance’ requirement. In many premises, the convenience of recording attendance is given precedence to the necessity of preventing unauthorized entry at all times. This leads to the continued use of obsolete access control technology, making the entire campus vulnerable to intrusion, albeit unknowingly. Due to the lack of clear understanding of and expectation from an ACS, vendors often make the most of the situation, installing systems that have gaping loopholes. The entry and movement of persons and/ or vehicles in a campus is of the utmost criticality for any security manager and a sound ACS must be the first step towards managing the same. Surveillance cameras may be the most visible element within security systems; however, they are reactive (mostly used after an incident has happened) – whereas an ACS has the ability to prevent that very incident. Since all security systems are now IP or IP based, ACSs have also gravitated towards Internet Protocol. This is a great news for both the security and network administrators; however, it does open up possible vulnerabilities, especially if there is an intersection of the data and security networks. Security managers have to be tuned into the IT security demands of their organizations and have to ensure that their ACS over IP poses no risks of virtual intrusion. Access control is a complex domain using multiple technologies – cards, readers, controllers, software, and the IT elements. Hence, it is best left to experts – however, here I lay down ten techno-functional parameters that must be considered when one decides to implement or upgrade to, a state-of-the-art ACS. While broadly any ACS can be judged by its adherence to these parameters, there are site-specific conditions that one must look into, to increase the efficacy. 1. Integration Integrability with other systems is the first parameter to consider while designing an ACS. The first integration point for any ACS should be with the fire/ smoke detection system. This is mandated by law. Unfortunately, ‘mandated by law’ is not a very strong phrase in India! This has led to tragic accidents in many organizations, retail environments and homes, but the learning is still not evident. Further, organizations do not have a clear standard operative procedure (SOP) in terms of the modalities and eventualities of such integration. Another integration point that can unlock the true potential of security systems is between video surveillance and access control. Though, modern video surveillance systems do have standard integration protocols with ACS (and vice versa) – these protocols must be harnessed to deliver better situational awareness. Many other integration touch points with ACSs are already being practiced – such as the one with T&A and ERP systems (which we will not delve into). However, one element of tight integration to be considered is with visitor management systems. As I never fail to repeat, the visitor management system really manages all the unknowns in your campus – and hence seamless integration of the visitor management system with the ACS is strongly required. Without intruding into the privacy of the visitor, a visitor management system should be able to clearly define what is expected of the visitor in your campus and what is the protocol when those expectations are not fulfilled. Ultimately, it is the campus! 2. Card vs. biometric Frankly there is no comparison at all – biometric ACSs are more secure, reliable and authentic than mere card based ACSs. However, this is a decision that involves change management and process re-engineering, which at times is difficult to achieve in brown-field projects. If card based ACS is to be used, multi technology smart card readers should be considered. It’s surprising to know that many organizations are still using proximity cards and readers, which have been proven to be eminently hackable. The decision of the card itself is an important one as it literally is the key to your premises. Smart contactless card platforms such as MiFare, DesFIRE and iCLass SE offer significantly higher security and encryption standards. 3. Card formats Organisations must ensure that they get completely involved in the important job of defining specific access card-bit formats. We have noticed that for the sake of manageability, security managers leave the task of card bit format definition to vendors, without perceiving the risk of vendor lock-in. 4. Information security Since ACSs have now become completely IP based, they have to be essentially treated as IT systems – and all the information security standards that the organization adheres to, have to be satisfied. Access data has to be treated with highest confidentiality standards. ACSs must quickly adopt the IP v6 protocol and use high encryption standards (min 128bit AES). Similarly, the IT elements such as the operating system, databases, processing and storage should all conform to the latest available versions to guarantee better support and security. 5. Reporting, alarms & alerts An ACS cannot be treated as a static system – it must have the capability to evolve as per the organisation’s growth. The software becomes critical in this sense, and hence it should have the ability to scale up, to be customized, and to be tuned to the growth requirements. This includes demands for varying report requirements that security managers will have for administrative management. Similarly, workflow based alerts and alarms are now a necessary feature for large campuses with a vertical security hierarchy. 6. Anti-duplication Biometric ACSs are the best to negate any chances for false entry. Even biometric ACSs do require de-duplication to ensure complete identification. Card based…

A Day in the Busy Life of A Traveler in 2020 At 5:30 a morning, her smartphone alarm drags Elise from a deep sleep, and into another busy day. The device unlocks when she looks at it with eyes barely open. At a shake of her head, the phone goes to ‘snooze.’ Two snooze cycles later, Elise stumbles to the kitchen where the coffee maker lights up with recognition to brew her favorite coffee. The radio, following suit, tunes to her favorite news program. Grabbing her briefcase, Elise heads out the door and into her car, where recognition software adjusts the driver’s seat position, switches on the radio and adjusts the volume. After a quick visit to the gym, she heads to work. A kiosk recognizes Elise and opens the gate with no fob or key card. Just like the gym earlier, another camera at the entrance identifies Elise and opens the door. Once inside, she takes an elevator that automatically whisks her to her seventh floor office which unlocks at her approach. At lunch, she moves rapidly through the automated cafeteria checkout. Food and beverages are scanned, and Elise uses facial payment to pay the tab with just a glance. Soon after, she’s on her way to the airport for a flight that will take her to meet clients in Tokyo the next day. The Eyes Have It – Creating an Easy & Personalized Airport Experience Today’s airports have become a painful experience for travelers. In 2020, that’s no longer the case. Once at the airport, Elise checks-in at the kiosk that identifies her through a face scan. Besides checking her in and giving her flight information, the kiosk also ensures Elise is not on a security watch list. She moves to the bag drop site, where another scan matches her bag with her ticket. Next, thanks to face recognition, she easily passes through the security checkpoint, what years earlier had been the most frustrating step in a system of ‘chokepoints.’ Now entering the main terminal, a nearby kiosk recognizes her and displays personalized gate and boarding information. Elise smiles, knowing she has time to relax, grab dinner and shop before the long flight. While shopping for the sweater she forgot at home, a store associate informs her of current promotions that might interest her based on her purchase history and opt-in loyalty program. The Case for Face Recognition Technology Elise’s story takes place in 2020, but the advanced face recognition solutions enabling that future are here today. They create a seamless, frictionless experience for everyday citizens like Elise as they make their way through their day – especially for travelers navigating increasingly busy airports. For businesses undergoing a digital transformation, face recognition technologies can drive increased revenues, simplify transactions, improve operational efficiency and create a paperless environment – all of which, again, improves the customer experience. At the same time, solutions combining surveillance cameras with face recognition software can elevate the level of public safety and security, not just in airports, but throughout society. Enterprises and government agencies can ensure only authorized personnel have access to their facilities, while retailers and entertainment venues can secure their facilities and protect customers. Airports are the melting pot where all these benefits come into play. In 2017 airlines world-wide carried more than 4 billion passengers, and demand for air travel is expected to double over the next 20 years. This will be a challenge for airports, which have to efficiently move people from one point to another, quickly and seamlessly. At the same time, 46 percent of passengers in 2017 still used the main ticket counters when they check in – and had their IDs checked manually – even though self-service options were available. Another 15 percent checked in through kiosks, most of which scan the ID, not the traveler. Queue management is a nightmare. In Europe’s busiest airports, wait times are often disrupted by staff shortages, time of year, or threat levels. At times, they are measured in hours rather than minutes, adding the stress of missed flights, rebooking and long layovers for travelers. Face recognition technology can sharply reduce those wait times, creating a frictionless experience for travelers from home to their final destination. It’s not just an issue of the travelers’ convenience and comfort, but also their safety. Airports are beset by threats, from terrorism to weapons being hidden in baggage. To keep passengers safe, security personnel require accurate verification of the identity of the person picking up the ticket, checking the bag, moving through the security checkpoint and boarding the plane. Face recognition technology is also used to take the accurate identifications and compare them to worldwide watch lists, even alerting security personnel to ensure the safety of passengers. In addition, it ensures accurate and fast verification for biometric air entry and air exit programs, speeding not only airplane boarding but also the customs and immigration processes. In this way, it not only ensures the security of every country and its citizens, it makes these processes fast and hassle-free for travelers. Touching Down After a StressFree, Relaxing Flight As the plane nears its destination, Elise wakes up and stretches. She orders a beverage and pays – naturally – using the on-board facial payment system. Relaxed, she deplanes in Tokyo and once again finds a frictionless process through the airport. Elise moves into the queue for immigration and customs, where her identity is quickly and accurately verified, enabling her to speed through the line. She now moves to the baggage claim carousel to get her luggage. Despite best efforts by the airlines, bags can be mishandled or stolen. In 2017, 22.7 million bags were mishandled worldwide, and 22 percent of all those were damaged or stolen. Spotting her bag, Elise feels assured it hasn’t been tampered with. Face recognition solutions can match travelers with their bags. The solutions can also monitor baggage claim areas to identify people damaging or stealing luggage, reducing thefts and increasing safety and…