Abstract
Computer-aided craniofacial reconstruction technology has very important application in the field of criminal investigation. But reconstruction a face from skull is not the end of work. The reconstructed face needs to be automatically identified in the missing population photo database. This paper proposed a reconstructed face recognition method based on deep learning. We trained a weighted fusion deep network for feature extraction, built two different neural network models for reconstructed face verification and use KNN for reconstructed face recognition. This paper uses 166 sets of data for experiments. In reconstructed face verification, the accuracy of using the Pseudo Siamese neural network is 98.33%. In reconstructed face recognition, the Top1 accuracy of the method using Pseudo Siamese neural network is 99.57%. Experiments show that the proposed method can effectively improve the accuracy of reconstructed face recognition.
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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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Liu, X., Zhao, S., Wang, S., Jing, Y., Feng, J. (2019). Reconstructed Face Recognition. 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_25
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DOI: https://doi.org/10.1007/978-3-030-31456-9_25
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