This paper describes a novel structural approach to recognize the human facial features for emotion recognition. Conventionally, features extracted from facial images are represented by relatively poor representations, such as arrays or sequences, with a static data structure. In this study, we propose to extract facial expression features vectors as Localized Gabor Features (LGF) and then transform these feature vectors into FacE Emotion Tree Structures (FEETS) representation. It is an extension of the Human Face Tree Structures (HFTS) representation presented in (Cho and Wong in Lecture notes in computer science, pp 1245-1254, 2005). This facial representation is able to simulate as human perceiving the real human face and both the entities and relationship could contribute to the facial expression features. Moreover, a new structural connectionist architecture based on a probabilistic approach to adaptive processing of data structures is presented. The so-called probabilistic based recursive neural network (PRNN) model extended from Frasconi et al. (IEEE Trans Neural Netw 9:768-785, 1998) is developed to train and recognize human emotions by generalizing the FEETS representation. For empirical studies, we benchmarked our emotion recognition approach against other well known classifiers. Using the public domain databases, such as Japanese Female Facial Expression (JAFFE) (Lyons et al. in IEEE Trans Pattern Anal Mach Intell 21(12):1357-1362, 1999; Lyons et al. in third IEEE international conference on automatic face and gesture recognition, 1998) database and Cohn-Kanade AU-Coded Facial Expression (CMU) Database (Cohn et al. in 7th European conference on facial expression measurement and meaning, 1997), our proposed system might obtain an accuracy of about 85-95% for subject-dependent and subject-independent conditions. Moreover, by testing images having artifacts, the proposed model significantly supports the robust capability to perform facial emotion recognition.
- Adaptive processing of data structures
- Facial expression
- Gaussian mixture model
- Human emotion recognition
- Probabilistic recursive neural network
ASJC Scopus subject areas
- Artificial Intelligence