One of the more important things the efficiency of your Face Recognition process will be heavily impacted by and definitely one you should pay a lot of attention to is the positioning of your cameras.
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Your cameras must be positioned so, that they will capture as many front-face images of your visitors, as possible. In other words, the front-face is always preferable to any other angle.
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Your cameras must be positioned in such a way, that your visitors' faces will be lit sufficiently.
The cameras send the visual data they collect to a server for the system to:
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detect any possible movements.
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locate a human face, if there is one.
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identify the face, if there is one (using 256 facial features or points).
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instruct the access control system to act in accordance with the identification result.
In addition to all the above capabilities, our system also determines the most fitting face recognition angle in each particular case. The system can also issue a warning if a visitor has used another person’s card to enter the premises.
There are several scenarios one should be prepared for that are almost guaranteed to be a frequent happening during the identification process.
As a dynamically growing company, we’ve noticed that you can barely ever be in time with the required adjustments to the system as new arrivals appear. Your HR folks may just be too busy with other things, and, even if your Face Recognition system is integrated with your HRM system, there is still one thing your office manager or security staff must definitely be able to perform quickly and on the fly:
There are several more functions that must be easily accessible to your office manager and security staff:
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They must be able to quickly adjust the system if it fails to identify a person completely (for example, due to poor photo quality) or identifies this person incorrectly (for example, by mistaking him or her for another person).
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They must be able to expeditiously re-train the system for just one single newly added person without having to wait for the whole of the system network to be re-trained overnight. This way, one will be able to prevent several similar recognition failures, occurring consecutively on the same day.
As you will have guessed, our product supports both these functions.
While working on our product, we also realized the great importance of just another system capability the system administrator must be able to easily access and use:
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The ability to quickly and effortlessly change the neural network configuration being used to one more suited to the current conditions, such as, for example, the type of source data or environment. For instance, our Face Recognition app allows quickly switching from SVM to Random Forest.
In order to be able to improve system performance, respond to a possible immediate threat, or avert a potential risk, you must be able to analyze the whole amount of the visual information your system has captured within a length of time. This can only be done if your Face Recognition application has advanced logging functionality (and this is precisely the case with our Face Recognition system).
Hence, is our next tip:
What is a good logging capability?
In order to encompass the full variety of possible scenarios, we have implemented 3 types of logs:
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A Movement-tracking log, - this log is created by default whenever a movement is detected.
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An Event log, created when the system detects a face for each of the persons present on the scene.
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A Face log, created by the system within an Event log as it identifies a face, indicating the recognition probability.