Features

A facial recognition system can be defined as a technology that can identify a person from a digital image by comparing and analyzing patterns based on the person’s facial characteristics.

Face Recognition can be used in a lot of different fields, such as smart surveillance, marketing, advertising, healthcare and many others.

Emotion recognition is a method used in computer vision that permits the software to “examine” the sentiments on a human face by analyzing image facial expressions.

Facial expression, in fact, is one of the most powerful, natural and universal signals for human beings to convey their emotional states and intentions.

Human activity recognition is an important yet challenging research topic in the computer vision community. It aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions.

It has various applications, spanning from activity understanding for intelligent surveillance systems to improving human-computer interactions.

Object recognition finds and identifies objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary from different view points.

Objects can even be recognized when they are partially occluded from view. This task is a challenge for computer vision systems and can be useful in a lot of different fields.

Scene detection is used for detecting transitions between shots in a video to split it into basic temporal segments. It helps video editors to automate the process of quickly splitting videos in bulk rather than editing it frame by frame by hand.

This brings down the process from a few hours just to a few minutes.

Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize a specific location of a given image.

It has a number of applications, ranging from autonomous driving and robot navigation to augmented reality and geo-localizing archival imagery.

Environment Recognition is focused on the task of assigning images to predefined categories. This is a very challenging problem in computer vision, in fact, understanding the world in a single glance is one of the most accomplished feats of the human brain.

The knowledge about the scene category can also assist in context-aware object detection, action recognition, and scene understanding.

Both cloud and on-premises licensing is offered to satisfy your company needs and help overcome the challenges you face every day

Thanks to Neural Network Technology, Recogneyes software can be trained to detect different audio and video elements. We continuously upgrade the skills that it can implement to satisfy your business needs.

Explicit and Violent Content Detection

Detects nudity, weapons and common sensitive content or specific sensitive content that needs to be found and flagged.

Speech Emotion Detection

Identifies emotions based on speech (what is being said) and voice tonality (how it is being said).

Sound Detection

Clears up telephony audio or noisy recordings. Identifies audio effects such as hand claps, speech, and silence.

Speech Recognition

Maps and understands which speaker spoke which words and when. Detects explicit text in the audio transcript.

Face recognition

Emotion Recognition

Action Recognition

Object Recognition

Scene detection

Place Recognition

Environment Recognition

Cloud & on-premise licensing

Future implementations

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