Computer vision core: Face Analysis

Automatic face analysis is a very active topic in computer vision research as it is useful in several applications, like biometric identification, visual surveillance, human-machine interaction, video conferencing and content-based image retrieval.

Facial analysis is one of the most important abilities used in our everyday lives.  Humans use face analysis for many different tasks. They recognize other people mainly from their faces, identify them as young or old, men or women, Caucasian or Asian. We may also see from the face whether a person is happy or sad, tired or nervous, feels pain, is telling the truth or lying. Faces are very important in social communications between humans. Emotional faces communicate both the emotional state and behavioral intentions of a person. The mouth movements are an important cue for speech recognition in noisy conditions, and hearing-impaired people can even read from lips. The head pose and gaze direction  provide information about the direction of attention.  Faces can also provide hints about health. For example, a person with   autism has difficulties to understand and express emotions.

Due to its great importance, facial image analysis has been a topic of intensive research in computer vision, image processing and related fields.  Among its potential areas of application are natural human computer/robot interfaces, biometric identification, image and video retrieval, multimedia management, video surveillance, forensics, audio-visual speech recognition, and affect-sensitive systems for various applications.

A common goal for our research on different areas of face analysis is to develop methods for unconstrained conditions (“in the wild”), allowing changes in illumination, head pose, image quality, and background.

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Selected References

Ahonen T, Hadid A & Pietikäinen M (2004) Face recognition with local binary patterns. In:  Computer    Vision, ECCV 2004 Proceedings, Lecture Notes in Computer Science  3021, 469-481.

Määttä J, Hadid A & Pietikäinen M (2012) Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1(1):3-10.

Zhao G & Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence  29(6):915-928.

Guo Y, Zhao G & Pietikäinen M (2016) Dynamic facial expression recognition with atlas construction and sparse representation. IEEE Transactions on Image Processing 25(5):1977-1992.

Pfister T, Li X, Zhao G & Pietikäinen M (2011) Recognising spontaneous facial micro-expressions. Proc. International Conference on Computer Vision (ICCV 2011), Barcelona, Spain, 1449-1456.

Li X, Chen J,  Zhao G, & Pietikainen, M (2014) Remote heart rate measurement from face videos under realistic situations. Proc.  IEEE Conference on Computer Vision and Pattern Recognition,. 4264-4271.

Zhou Z, Hong X, Zhao G & Pietikäinen M (2014) A compact representation of visual speech data using latent variables. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(1):181-187.

 

 

 

Last updated: 19.9.2016