Face recognition based on BOW and SURF local features

doi:10.3969/j.issn.1000-1565.2017.04.013基于词包模型和SURF局部特征的人脸识别

Abstract

Abstract: To overcome the limitations of traditional face recognition methods for real-time, a face recognition method which based on speed up robust features and bag-of-word model was proposed.Image after preprocessing, we used SURF to extract key points of images and corresponding feature descriptors automatically.Further, bag-of word model was used to code the descriptors into visual words as local features of the face.Finally, K-Nearest Neighbor algorithm was adopted to recognize the human faces.The proposed method is validated with both CMU-PIE dataset and dataset collected in the laboratory.It can achieve 97.5% and 99.3% recognition rates on these two datasets, respectively.In average, it took less than 0.108 s for feature extraction and less than 0.017 s for matching.The results indicate that the proposed method not only precise moreover fast, and had better stability and effectiveness.

Key words: face recognition, bag-of-word model, SURF, local features, K-NN

CLC Number:

TP391

LIU Cuixiang, LI Min, ZHANG Fenglin. Face recognition based on BOW and SURF local features[J]. Journal of Hebei University (Natural Science Edition), 2017, 37(4): 411-418.

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