An Improved Facial Expression Recognition Algorithm

摘要

The recognition of facial expression images is susceptible to non-uniform illumination factors, which may reduce the recognition rate. In view of this, an improved facial expression recognition algorithm is proposed. Firstly, the pattern-oriented edge magnitudes (POEM) histogram of the corresponding facial expression image is obtained through calculating the characteristic quantity of the facial expression image by the POEM. The histogram is created as the POEM texture histogram of the central characteristic point and the texture characteristic information of the facial expression feature points are obtained Secondly, the improved incremental non-negative matrix factorization (IINMF) algorithm is used to train the category information of face image samples to extract the face image representation vector. Canonical correlation analysis (CCA) is then used to combine the characteristic information of the POEM texture histogram and the eigenvector of the facial expression image extracted by IINMF to obtain the syncretic eigenvector of the facial expression image. Finally, the nearest neighbor classifier is used to classify and obtain the final identification result. The experimental results show that the proposed algorithm has a high recognition rate for facial expression recognition under non-uniform illumination and has excellent robustness and real-time results.