Deep Machine Learning – reliable realtime object and face recognition

Just imagine that objects and people in the warehouse are being automatically recognized with their essential characteristics by a camera in real time. A wide range of innovative application fields is available: starting from automatic recording of material flow, stock control, maintenance of master data together with support for the staff during entry and quality assurance, privacy protection in video material up to access control and time recording. These benefits lead to maximized quality and reduced costs.

With help of the revolutionary realtime object and face recognition from Logivations enabled by Deep Machine Learning is this no longer a dream, but a reality!

Machine learning network


Cameras & algorithms instead of Scanners and RFID for object identification

object recognition


Making people unrecognizable in videos is hard. Once a person’s face is visible - even for less than a second – this person can be easily identified by a human watcher. Thus, a computer algorithm needs to look for face templates and try to recognize these templates in the pictures. Significant complexity is not only about the object itself, but also about its position, location and lighting conditions.

Conventional solutions are based on a few predefined templates. They are not comprehensive in considering these situational factors. That is why they can´t offer the necessary reliability to control such crucial cases as e.g. privacy protection, access control or automatic execution of the processes (see example below):

The Deep Machine Learning algorithm from Logivations practices like a human being and further learns after each positive recognition, instead of just being limited to predefined templates. As a matter of fact, it is trained to recognize objects and faces based on real examples.

We succeeded in providing exactly this reliable recognition in realtime and out of the Cloud by using Machine Learning and neural networks from the current developments. With this our object and face recognition goes far beyond the usual functional spectrum of similar solutions, as besides detection it can also capture quantity, positions and characteristics of objects.


Here’s how most current solutions work: They use the so-called Viola-Jones algorithm. Typically on a face, the eyes are darker than the nose, thus the algorithm looks for such patterns:

Face recognition algorithm

Once a part of an image contains such a pattern, it is classified as a face. This works well for faces with no rotation, and as a bonus is quite fast.

But, what happens with faces that can be viewed from the side? Here, only one eye is visible, so the pattern won’t be matched, and no face is detected. Thus, another pattern has to be added for profile faces. This is already quite hard, as there is no simple pattern applicable to every profile faces.

Nearly impossible however is finding patterns for unusual head positions: someone that is looking at the ground, or up a wall. These situations are very hard to recognize with the Viola-Jones algorithm.

That’s the issue: We, as humans, can recognize a known person even if we can only see a profile view of their face. Thus, an algorithm useful for privacy protection has to find all those cases as well and blur them.


Logivations set out to build a better solution. With latest technologies, such as neural networks we used a database of more than 500.000 images of humans to train an algorithm to find patterns for heads on its own – in all positions, rotations and lighting conditions imaginable. Here’s an excerpt of the patterns found:

Example of faces and objects pattern

As one can see, instead of black-and-white pixels, our algorithm found lots of complex patterns. Thus, it can detect heads even in very non-usual positions. This solution easily detects heads where other algorithms fail.

Worker on warehouse 3/4 view. Head recognition
Worker on warehouse in profile. Head recognition

In these images, 0.997 means the algorithm has a 99.7% confidence in the detection.

Such an algorithm requires considerable amounts of resources. Using the power of the cloud we can provide a fast and seamless processing of the videos.

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