Shojaeilangari, S.; Wei-Yun Yau; Nandakumar, K.; Jun Li; Eam Khwang Teoh, "Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learning," in Image Processing, IEEE Transactions on , vol.24, no.7, pp.2140-2152, July 2015 doi: 10.1109/TIP.2015.2416634
Abstract:
Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications like human-computer interaction, automated tutoring
systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real world
applications. To robustly recognize facial emotions in real-world natural situations, this paper proposes an approach called Extreme Sparse Learning (ESL), which has the ability to jointly learn a dictionary (set of basis) and a non-linear classification model. The proposed approach combines the discriminative power of Extreme Learning Machine (ELM) with the reconstruction property of sparse representation to enable
accurate classification when presented with noisy signals and imperfect data recorded in natural settings. Additionally, this work presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able
to achieve state-of-the-art recognition accuracy on both acted and non-acted facial emotion databases.
License type:
PublisherCopyrights
Funding Info:
A*STAR
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.