Person-Similarity Weighted Feature for Expression Recognition

摘要

In this paper, a new method to extract person-independent expression feature based on HOSVD (Higher-Order Singular Value Decomposition) is proposed for facial expression recognition. With the assumption that similar persons have similar facial expression appearance and shape, person-similarity weighted expression feature is used to estimate the expression feature of the test person. As a result, the estimated expression feature can reduce the influence of individual caused by insufficient training data and becomes less person-dependent, and can be more robust to new persons. The proposed method has been tested on Cohn-Kanade facial expression database and Japanese Female Facial Expression (JAFFE) database. Person-independent experimental results show the efficiency of the proposed method.