Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a chal...Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).展开更多
A new method to extract person-independent expression feature based on higher-order singular value decomposition （HOSVD） is proposed for facial expression recognition. Based on the assumption that similar persons ha...A new method to extract person-independent expression feature based on higher-order singular value decomposition （HOSVD） is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression （JAFFE） database. Person-independent experimental results show the superiority of the proposed method over the existing methods.展开更多
基金supported by the Academy of Finland(267581)the D2I SHOK Project from Digile Oy as well as Nokia Technologies(Tampere,Finland)
文摘Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).
基金supported by National Natural Science Foundation of China (6087208460940008)+2 种基金Beijing Training Programming Foundation for the Talents (20081D1600300343)Excellent Young Scholar Research Fund of Beijing Institute of Technology (2007Y0305)Fundamental Research Foundation of Beijing Institute of Technology (20080342005)
文摘A new method to extract person-independent expression feature based on higher-order singular value decomposition （HOSVD） is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression （JAFFE） database. Person-independent experimental results show the superiority of the proposed method over the existing methods.
