ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10(02):193-200.[doi:10.3969/j.issn.1673-4785.201405060]





A deep belief network-based heterogeneous face verification method for the second-generation identity card
张媛媛 霍静 杨婉琪 高阳 史颖欢
南京大学 计算机软件新技术国家重点实验室, 江苏 南京 210023
ZHANG Yuanyuan HUO Jing YANG Wanqi GAO Yang SHI Yinghuan
State Key Laboratory for Software Technology, Nanjing University, Nanjing 210023, China
face recognitionmultimodesdeep learningdeep belief network
The objective of the face verification method for the second-generation identity card is to determine whether the original head-photo stored in the corresponding identity card image and the currently captured head photo of the card-holder by using a video camera image actually belongs to the same person or not. To obtain a good verification result for the heterogeneous face verification method is a very challenging task because the two different types of images belong to two different modalities (e.g., different image resolutions, different illumination conditions). Considering the difference of trans-modal face images, it is hard to use traditional feature extraction methods to extract discriminative feature for description of images with different modes. Traditional feature extraction methods cannot distinguish images exactly. In this paper, a deep learning-based face verification method is proposed. The proposed deep learning-based face verification method integrates the deep belief network (DBN), which employs unsupervised greedy layer-by-layer training for high-level feature extraction of face photo and combines the popularly used image preprocessing and similarity measurement technologies to realize the purpose of face verification. The results were evaluated on a real dataset with two different modalities of 256 different people. This method outperforms the traditional principal component analysis (PCA) and linear discriminant analysis (LDA) methods with 12% and 8% improvements in terms of the verification accuracy, respectively. The results validated the advantage of the proposed method, especially when the amount of entries increases.


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更新日期/Last Update: 2015-06-15