Reading Faces
Apart from being the channel used to identify other members of the species, the human face provides a number of signals essential for interpersonal communication in our social life. The face houses the speech production center and is used to regulate the conversation by gazing or nodding, and to interpret what is being said by lip reading. It is our direct and naturally preeminent means of communicating and understanding someone’s affective state and intentions solely based on a displayed facial expression is being shown.
1| Face Detection
The first step of our Face Analysis system consist of accurately finding the location and size of faces in arbitrary scenes under varying lighting conditions and complex backgrounds. Face detection, combined with eye detection, gives us a perfect starting point for the following facial modeling and expression analysis.
2| Face Modeling
Since the beginning of the 90s, VicarVision has been developing and fine tuning an advanced 3D face modeling technique. In its current state, the technique is capable of automatically modeling a previously unknown face, in real-time, using a 3D face model with over 500 keypoints. This corresponds to a parametric representation of a face by modeling the geometry of rigid features on the face. This makes the analysis of facial expressions, head pose and gaze detection possible and proves to be useful in a wide range of applications.3a| Facial Expression Recognition
Facial expressions play a crucial role in the social communication between people. Our face modeling technique allows the classification of the 6 most prominent universal facial expressions related to the following emotional states: anger, disgust, fear, joy, sadness and surprise plus a neutral state. The occurrence and confidence of these non-verbal communication forms are classified continuously between zero and one.
Below you can see the accuracy of our system on the Radboud Faces Database [1].
Proportion of agreement between the facial expressions scored manually by the annotators of the Radboud Faces Database [2] (horizontally) and the expressions scored by FaceReader version 4 (vertically).
| Neutral | Angry | Happy | Sad | Scared | Surprised | Disgusted | |
|---|---|---|---|---|---|---|---|
| Neutral | 87.1% | 2.3% | 0.6% | 9.9% | 0.6% | 0.0% | 2.3% |
| Angry | 7.0% | 93.0% | 0.0% | 1.8% | 0.0% | 0.0% | 5.8% |
| Happy | 0.0% | 0.0% | 95.9% | 0.0% | 1.2% | 0.0% | 0.0% |
| Sad | 4.7% | 2.9% | 0.0% | 87.1% | 4.1% | 0.0% | 0.6% |
| Scared | 0.0% | 0.0% | 0.0% | 0.6% | 84.4% | 2.3% | 0.0% |
| Surprised | 0.6% | 0.0% | 0.0% | 0.0% | 3.5% | 94.2% | 2.3% |
| Disgusted | 0.0% | 0.6% | 0.6% | 0.6% | 0.0% | 0.0% | 84.8% |
3b| Gender and Age Detection
The generated face model also supplies insights regarding the complex facial attributes like the gender and age of the subject. Using our technology a single still image is enough to reliably estimate the gender and age of person. Moreover, with additional data in the form of video could further improve the performance.4| Eye Tracking
Eye tracking allows to find the direction where one is looking towards by measuring the position and movement of eyes with respect to the head. Our technology offers a reliable method to track the natural eye movement using the generated facial model and the relative eye location. This tool addresses a wide range of applications in psychology, market research and usability analysis.
