This week I had a chance to meet with Chris Henderson a Biometric specialist who recently joined Hitachi Vantara in Australia. Chris was no stranger since we had worked together in the past and he has been working on some biometric projects for Hitachi Australia around Finger Vein identification and Live Face Matching (LFM) systems as a contractor.
Chris has been working on a Live Face Matching system with a large retailer in Australia to reduce shrinkage using video analytic technology from Hitachi. Shrinkage is a term used in the retail industry for the discrepancy between the dollar amount of the book inventory and the physical inventory in the store. This discrepancy is caused by errors, spoilage and theft. The biggest cause for shrinkage is shoplifting, followed by employee theft. In Australia, shrinkage amounted to a loss of $4.7 USD billion last year with specialty store like electronics, hit the hardest. I looked up the numbers for the United States where Forbes reported $46.8 billionin losses last year. Shrinkage losses have a direct hit on profit and are a special concern in the retail industry where margins are razor thin.
Most retail stores have CCTV cameras in the store which are used after the fact to identify suspicious activity and prosecute shoplifters. Unfortunately, this does not prevent shop lifting activity. This is where a live face-matching system, can help. The Forbes article noted the following about shop lifting:
- 41% of retailers surveyed reported increases in overall inventory shrink.
- The average cost per shoplifting incident doubled to $559.
- 60 percent of known shoplifters were detected entering at least two separate locations of the same retail chain.
- 20% 0f known shoplifters visited three or more locations of the same retail store.
A Live Face Matching system that is connected to all the stores locations can share the templates of know shoplifters and alert the security personnel as soon as they enter the store and allow security personnel to pre-empt the shoplifter, by providing closer attention or customer care. This is safer and less likely to cause an incident or risk of litigation, than confronting a shoplifter after the act.
A large store in Australia was dealing with frequent occurrences of antisocial behavior and theft. Retail theft was amounting to about 2% of turnover which is the same worldwide. Their network of security cameras was not deterring these incidents. The store deployed a solution that combined an advanced Live Face Matching system from Hitachi with advanced video data analytics and facial recognition technology. During the first three months the store achieved significant success in identifying and apprehending a number of repeat offenders News of this activity spread throughout the community which deterred other thieves.
With video systems, there are privacy concerns. Depending on the jurisdiction, a person visiting or entering a store automatically opts in to being videoed. A Face matching system does not store a picture. It stores data about the face like the distance between the eyes and creates a template of data points. The number of data points and the algorithms that are used are proprietary to the vendor and are stored encrypted. In that sense the data stored is anonymous. Templates of offenders who have been observed shop lifting in the past are entered in a data base and an alert is given when a match occurs as the offender enters the store. The data base is shared across the store’s locations, since offenders often target other locations of the same store where they are familiar with the store’s method of operations.
Live Face Matching from Hitachi analyzes live video to recognize registered individuals for security or operational purposes. Highly accurate and able to run on a variety of different camera feeds, LFM is a powerful tool for law enforcement, corporate security, identity-based operations, and customer services. High accuracy, frame rate capacity, and affordability make LFM a clear choice for your facial recognition solution.