Emotion estimation detects facial expressions from images or videos and returns the probability distribution of each of the six universal emotions: happiness, sadness, anger, fear, surprise, and disgust, and additionally neutral.
There are certain common features in human faces that distinguish male faces from female ones. FaceAnalysis uses advanced machine learning techniques to provide you with gender detection.
Landmarks such as the location of the pupils, eye corners, lip boundaries, etc. change with age. Our algorithm was trained on a large database of different faces to detect the approximate age of a person based on such features.
FaceAnalysis detects faces in images or video and then uses face tracking and action units to accurately provide gender, emotions and age for the faces in roughly frontal position.
This allows you to track and respond to human behavior in real time, build engaging customer experiences and gain deeper insights into the effect of various stimuli on emotions.
Face analysis is the process of using computer algorithms and machine learning techniques to analyze human faces in real time, as well as in images or videos. Through Visage Technologies’ face analysis, detailed data for people’s gender, age, and emotions can be estimated. The analysis is based on the location of facial landmarks such as the pupils, eye corners, and lip boundaries.
Emotion estimation is the process of using machine learning algorithms to detect facial expressions from images, videos, or in real time, and then presenting the probability of each of the six universal emotions: happiness, sadness, anger, fear, surprise, disgust, and additionally neutral. It works by using complex computer algorithms that recognize patterns in the face to determine which emotion is being expressed. And if you’re looking for other or more complex emotions, please don’t hesitate to contact us.
When compared to other similar AI solutions, our age estimation algorithm is among the best, as confirmed by this scientific paper published in Nature Scientific Reports (the fifth most-cited journal in the world). Even though they did not use the latest version of our visage|SDK, our age estimation algorithm performed exceptionally well. To be more precise, our algorithm was found to be the most accurate overall, as well as for neutral and smiling faces specifically, and we performed especially well in estimating the age of older people.
On average, the age estimation accuracy provided by FaceAnalysis is +/- 4.5 years. For some ages, the accuracy can even be as high as +/- 2 years. Generally, the accuracy of age estimation depends on various conditions such as age group, lighting, head pose, etc.
Yes, our face analysis technology is customizable. Visage Technologies’ consulting and custom development services are available to adapt the technology in terms of precision, performance and any other requirements in order to meet the needs of your specific applications.