Facial Action Unit Intensity Detection by Extracting Complimentary Information using Distance Metric Learning

ABSTRACT

Facial action unit intensity detection is the key concern of researchers as it gives a much broader information about facial expressions of the individual. In the proposed work, an attempt is done to detect the intensity of the facial action unit by combining geometric deformations and appearance deformations of facial features. Thin plate spline is adopted for extracting geometric deformations, and Gabor filters are adopted for extracting appearance deformations. To combine both the description mentioned above, a metric learning method is used to combine the descriptors in such a way that complimentary information is extracted from them. Moreover, it also maps the features to higher discriminative space. The features are applied to support vector machine for the facial action unit intensity detection. The proposed approach is evaluated on the popularly accepted database: DISFA database and UNBC shoulder pain database. The results are compared with the state-of-the-art approaches to prove the efficacy of the suggested approach.

KEYWORDS:

• Distance metric learning
• Facial action units intensity detection
• Facial feature descriptors
• Support vector machine

Additional information

Notes on contributors

Neeru Rathee

Neeru Rathee has done her PhD on facial expression analysis from GGSIPU and has done her MTech from DU. She is currently working with Maharaja Surajmal Institute of Technology, C-4, Janakpuri, New Delhi. Her research area includes image processing, computer vision and artificial intelligence.

Dinesh Ganotra

Dinesh Ganotra has done his PhD from IIT Delhi and is currently working as an assistant professor with Indira Gandhi Delhi Technical University for Women (formerly Indira Gandhi Institute of Technology). His research area includes panorama vision, speech processing and image processing.Email: [email protected]

Ajay Rathee

Ajay Rathee has done hisMTech from IIT Delhi and is currently working in DRDO. His research area includes laser technology and computer vision.Email: [email protected]