Abstract
Craniofacial reconstruction technology is very important in the field of criminal investigation. But reconstruction a face from skull is not the end of work. The key technology is the similarity measurement between reconstructed 3D face and 2D face image. It can not only be used to retrieve the most similar face image from missing population database but also can be used to value the reconstruction methods. We built a 3D reconstructed face dataset, trained a deep face feature extraction model and built a neural network for similarity measurement. Firstly, the reconstructed 3D face and 2D face image need to be preprocessing. Secondly, deep network is designed for similarity measurement. Finally, we tested the proposed model. The accuracy of the similarity between two kinds of face images was 96.67%. Experiments show that the proposed neural network model can effectively measure the similarity between two kinds of face images.
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Acknowledgment
This work is supported by Shaanxi Natural Science Foundation No. 2018JM6061, Special Scientific Research Program of Shaanxi Education Department No. 2013JK1180 and Qingdao Municipality’s Independent Innovation Major Project of China (2017-4-3-2-xcl).
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Zhao, S., Liu, X., Wang, S., Jing, Y., Feng, J. (2019). Similarity Measurement Between Reconstructed 3D Face and 2D Face Based on Deep Learning. 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_28
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DOI: https://doi.org/10.1007/978-3-030-31456-9_28
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