Face detection

In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class.

Then the genetic algorithm is used to generate all the possible face regions which include the eyebrows, the iris, the nostril and the mouth corners.

[5] Face detection is also useful for selecting regions of interest in photo slideshows that use a pan-and-scale Ken Burns effect.

OpenAI's CLIP model[9] exemplifies the use of deep learning to associate images and text, facilitating nuanced understanding of emotional content.

For instance, combined with a network psychometrics approach, the model has been used to analyze political speeches based on changes in politicians' facial expressions.

[10] Research generally highlights the effectiveness of these technologies, noting that AI can analyze facial expressions (with or without vocal intonations and written language) to infer emotions, although challenges remain in accurately distinguishing between closely related emotions and understanding cultural nuances.

Automated lip reading has applications to help computers determine who is speaking which is needed when security is important.