OpenCV Tutorial | OpenCV using Python- Limitation in the Face Detection

The Facial Recognition System is essential nowadays, and it has come a long way. Its use is essential in quite some applications, for example – Photo retrieval, surveillance, authentication/access, control systems etc. But there are a few challenges that have continuously occurred during image or face recognition system.

These challenges need to be overcome to create more effective face recognition systems. The Following are the challenges which affect the ability of Facial Recognition System to go that extra mile.


The illumination plays an essential role during image recognition. If there is a slight change in lighting conditions, it will make major impact on its results. It is the lighting to vary, and then the result may be different for the same object cause of low or high illumination.


The background of the object also plays a significant role in Face detection. The result might not the same outdoor as compared to what is produces indoors because the factor – affecting its performance-change as soon as the locations change.


The facial recognition system is highly sensitive to pose variations. The movement of head or different camera positions can cause changes of facial texture and it will generate the wrong result.


Occlusion means the face as beard, mustache, accessories (goggles, caps, mask, etc.) also interfere with the estimate of a face recognition system.


Another important factor that should be kept in mind is the different expression of the same individual. Change in facial expressions may produce a different result for the same individual.

In this tutorial, we have learned about the OpenCV library and its basic concept. We have described all the basic operation of the image. In the next tutorial we will learn about the face recognition and face detection.