5 Companies Pioneering Tech in Developing Countries
Within the past five years, more than 250,000 children have gone missing in India. Various factors make it difficult for families to find their loved ones. To combat this worrying epidemic, a Chennai IT developer named Vijay Gnanadesikan developed a closed application called FaceTagr. The app uses facial recognition technology to identify missing people. FaceTagr has amassed a photo database of nearly 300,000 missing children and has already identified and returned more than a hundred.
An app that tracks missing children
Vijay and his team have been surfing various central and state government websites and social networking sites to create a bank of photos of nearly three lakh children, some of them in government or private homes but missing from their families. “If you feed in a photo, our software automatically links it to children who look similar in our database,“ said Vijay , pointing to a photo of a four-year-old child beggar in Allahabad he chanced upon on Facebook. Vijay and his team have been surfing various central and state government websites and social networking sites to create a bank of photos of nearly three lakh children, some of them in government or private homes but missing from their families. “If you feed in a photo, our software automatically links it to children who look similar in our database,“ said Vijay , pointing to a photo of a four-year-old child beggar in Allahabad he chanced upon on Facebook. It matched with the picture of a boy on the Centre's Track Child portal reported missing from Haryana by his parents.
Indian face recognition - They can claim to be a step ahead of Tokyo.
Chennai’s T. Nagar, arguably India’s biggest shopping district by revenues and crowded on any given day, gets even more packed in festival seasons as thousands throng its saree and jewellery stores. Every year, Deepavali, less than three months away this year, presents the perfect hunting ground for pickpockets and other petty thieves — and a headache for the local police.
This time, however, the city police have reason to believe it has a handle on things. It has a technology that analyses CCTV footage to spot, in real time, people with a criminal history visiting the T. Nagar area. “We are matching real-time CCTV video footage with our criminal database using the FaceTagr system and if any criminals are identified in that area, we get an immediate alert and we can further investigate,” says P Aravindan, deputy commissioner of police. Last year, FaceTagr, a face recognition software developed by an eponymous Chennai company, was used in a few areas with results that convinced the police to spread it to all of the T Nagar area, he adds.
FaceTagr app: Chennai police’s bright spark helps nab elusive criminals
The case of serial chain-snatching incidents, in which a 55-year-old Srinivasan alias Burma Srinivasan from Korrukupet who had been involved in the crime for over 20 years, was arrested, was solved with the help of the Facetagr application, which was introduced by the State police. The only clue the police had in this case was CCTV footage in which the same person was spotted snatching chains in the incidents reported at Valsaravakkam, KK Nagar and other parts of the T Nagar police district.
“Since he already had cases registered against him, his photo was stored in the database. And the CCTV footage was processed through the ‘Facetagr’ application and the suspect was identified in 150 milliseconds,” said P Aravindan, Deputy Commissioner of Police.
The future arrives at a Govt. institution
Every day, when the students of the Government Presidency Girls Higher Secondary School in Egmore, set foot in the school building, they realise the power of technology. Their school attendance is marked through face recognition and it provides additional layer of security.
Armed with anti-crime software, cops nab 16 history sheeters
Tiruvannamalai: Nabbing a criminal in a chock-a-block festival town is like finding a needle in a haystack. But, Triuvannamalai
police said they nabbed 16 history-sheeters during the Karthigai Mahadeepam festival, which saw lakhs of devotees thronging
the temple town.
Police were able to achieve this with meticulous planning and the aid of ‘Face TAGR’, a face detection software.
Police connected the surveillance cameras installed in strategic locations in and around Sri Arunachaleswarar temple with a
server loaded with more than 60,000 active criminals in the state. Police personnel attached to crime wing manned three
control rooms inside the temple, Tiruvannamalai town station and a mobile unit.
By this, they ensured that no devotee fell prey to criminals who indulge in snatching, pickpocket, robbery or theft.
“It was an incident-free festival, except two cases of missing gold chains. No case of robbery, theft or snatching was reported
this year festival. The software helped to prevent crimes and also made many criminals stay away from Tiruvannamalai during
the festival,” Tiruvannamalai superintendent of police R Ponni told TOI.
Last year, around 40 cases of snatching, theft and attention diversion were filed, she added.
Police nabbed four history-sheeters from the Mada Streets during the temple car festival on November 29. Similarly, they held
12 criminals from November 29 and December 3 in and around the temple. They were from Perumbalur, Vellore, Salem,
Dharmapuri, Tanjore, Madurai and Tiruvannamalai districts. Police invoked section 379 (punishment for theft) of IPC and
Recollecting the arrest of a 21-year-old history sheeter, Pathiban of Erode, deputy superintendent of Polur N Kottieswaran said
the control room received an alert after the surveillance camera connected found a match of an accused in the database. “We
received all the details of the person and alerted policemen on patrol near the temple. We had his location and identification
marks such as the dress colour. He was secured within 30 minutes,” he said.
Fast and Accurate Enterprise Face Recognition. Advanced Video Analytics. Dashboards and Mobile App.
Automated, Real-time Enterprise Face Recognition with LFW Accuracy Score of 99.4%. Deploy on feeds from any type, brand of camera.
Uses existing CCTV cameras to:
People Counters, Age Estimation, Gender Identification, Mask or not, Retail Analytics, Social Distancing
Track from cars to jewellery, helmets to cups. Analyze and identify objects by type etc. Used in car parking to security monitoring.
LFW Accuracy Score: 99.4 %
Automated, Real-time, Contact-less.
Works on Photos & Videos.
Detects faces in spite of disguise – hair, beard, spectacle etc.
CLOUD – Deployed on Secure Enterprise Servers.
EDGE – Distributed processing. Great for reduced bandwidth applications.
ON-PREMISE – On-site deployment. Server sized based on number of cameras.
Camera Agnostic: Works with any standard CCTV, P2P, Webcams, Mounted & Mobile cameras.
Works with just one reference photo.
Easy to deploy and scale.
FaceTagr uses its proprietary face recognition technology to compare a photograph or video to a database of reference images and provides results instantly. Provides 1:1, 1:N recognition on images, videos through web portal, mobile apps and API.
FaceTagr’s CV platform provides insights and analytics from existing CCTV video feeds. Heat-maps, object detection, counters, zone watching are some of the most popular use cases from our customers.
Automatically analyze video feeds for identifying people of interest to get insights to plan your operations better or improve sales. Our proprietary processes of deployment makes it easier to deploy, making use of existing infrastructure and our dashboard and AI analytics provide what you need to improve your organization.
Recognize objects with precision, AI analytics to take action on. From helmets to footwear, cars to cups we track them all. Different industries require an automated way to track objects. Our platform provides data and analytics catering to the needs.
FaceTagr is camera and processing hardware agnostic. FaceTagr is very lightweight and the hardware can be customized based on uses, deployment options and parameters to track. This makes FaceTagr very scalable as well.
Take a photo or upload from phone. Works on 18,000 mobile models. Checks within seconds and provides results. Easy to deploy and use. Highly scalable. KPI dashboard, alerts and mobile app notifications.