Abstract
In order to combine with different feature extraction methods, in this paper, we propose a new method using similarity calculation between multiple features. We regard face recognition as a maximum-a-posteriori (MAP) problem and the de-pendency between different features is defined by a markov chain. We construct a matching similarity function T which helps us finding a better matching image. Experiments were tested using AR database and the results have shown that our recognition rate is higher, especially robust to small occlusion and noise.
Yigan Li, master student main research interests: image processing face recognition.
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Li, Y., Wang, Z. (2019). A Face Recognition Workflow Based Upon Similarity Measurement. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_31
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DOI: https://doi.org/10.1007/978-3-030-31456-9_31
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