Face recognition is one of the most demanded computer vision technologies for security systems and web-search. A list of potential applications includes:
Two application scenarios for face recognition should be distinguished:
In general each scenario can be reduced to the other one. But each scenario sets different requirements on face recognition algorithm, which results in different training and tuning procedures.
Most face recognition algorithms share the same operation procedure:
Usually several steps after face detection and before verification are collectively named "Enrollment", meaning that a new face image is processed and stored in the database.
As face recognition algorithm is a pipeline, constructed from several consecutively applied algorithms, performance depends on accuracy of each stage. Technical details of each step are usually a commercial secret and described in a very general way.
The main performance characteristics of face identification algorithms are descriptor computation speed (Enrollment), descriptors comparison time, precision of person identification
Performance of face identification depends on many factors: image resolution (more correct face size on an image), image sharpness, viewing angle (frontal or at an angle), lighting, mimic, and size of the gallery.
That is why an "abstract" recognition precision, mentioned in different news releases, without binding to a concrete face image dataset, used for measurements, does mean absolutely nothing. Under some ideal conditions a recognition precision can be even higher than human performance and reach the precision, demonstrated by eye iris recognition. But in practice the image recognition precision is significantly lower, which limits the range of possible applications.
We have developed our own face recognition algorithm. To evaluate its performance on your application we offer to test our software on your image dataset.