浏览全部资源
扫码关注微信
收稿：2020-02-19，
修回：2020-3-3，
录用：2020-3-10，
纸质出版：2020-12-16
移动端阅览
目的
2
相对于其他生物特征识别技术，人脸识别具有非接触、不易察觉和易于推广等特点，在公共安全和日常生活中得到广泛应用。在移动互联网时代，云端人脸识别可以有效地提高识别精度，但是需要将大量的人脸数据上传到第三方服务器。由于人的面部特征是唯一的，一旦数据库泄露就会面临模板攻击和假冒攻击等安全威胁。为了保证人脸识别系统的安全性并提高其识别率，本文提出一种融合人脸结构特征的可撤销人脸识别算法。
方法
2
首先，对原始人脸图像提取结构特征作为虚部分量，与原始人脸图像联合构建复数矩阵并通过随机二值矩阵进行置乱操作。然后，使用2维主成分分析方法将置乱的复数矩阵映射到新的特征空间。最后，采用基于曼哈顿距离的最近邻分类器计算识别率。
结果
2
在4个不同人脸数据库上的实验结果表明，原始人脸图像和结构特征图像经过随机二值矩阵置乱后，人眼无法察觉出有用的信息且可以重新生成，而且融合方差特征后，在GT（Georgia Tech）、NIR（Near Infrared）、VIS（Visible Light）和YMU（YouTuBe Makeup）人脸数据库上，平均人脸识别率分别提高了4.9%、2.25%、2.25%和1.98%，且平均测试时间均在1.0 ms之内，表明该算法实时性强，能够满足实际应用场景的需求。
结论
2
本文算法可在不影响识别率的情况下保证系统的安全性，满足可撤销性。同时，融合结构特征丰富了人脸信息的表征，提高了人脸识别系统的识别率。
Objective
2
Along with the wide usage of various digital image processing hardware and software and the continuous advancements in the field of computer vision
biometric recognition has been introduced to solve identification problems in people's daily lives and has been applied in the fields of finance
education
healthcare
and social security
among others. Compared with iris
palm print
and other biometric recognition technologies
face recognition has received the most attention due to its special characteristics (e.g.
on-contact
imperceptible
and easy to promote). Given the wide usage of mobile Internet
cloud face recognition can achieve high recognition accuracy requires a large amount of face data to be uploaded to a third-party server. On the one hand
face images may reflect one's private information
such as gender
age
and health status. On the other hand
given that each person has unique facial features
hacking into face image databases may expose people to threats
including template and fake attacks. Therefore
how to boost the privacy and security of face images has become a core issue in the field of biometric recognition. Among the available biometric template protection methods
the transform-based method can simultaneously satisfy multiple criteria of biometric template protection and is presently considered the most typical cancelable biometric algorithm. The protected biometric template is obtained via anon-invertible transformation of the original biometric that is saved in a database. When this biometric template is attacked or threatened
a new feature template can be reissued to replace the previous template by modifying the external factors. To guarantee the security of the face recognition system and improve its recognition rate
this paper investigates a cancelable face recognition algorithm that integrates the structural features of the human face.
Method
2
First
structural features are extracted from the original face image by using its gradient
local binary pattern
and local variance. By taking the original face images as real components and the extracted structural features as imaginary components
a complex matrix is built to represent the face image. To render the original face images and contour of their structural features invisible
the complex matrix is permuted by multiplying it by a random binary matrix. Afterward
complex 2D principal component analysis (C2DPCA) is performed to project a random permuted complex matrix into a new feature space. The 2DPCA result for the scrambled complex face matrix is theoretically deduced and verified to be the result of original complex face matrix 2DPCA multiplied by a random binary matrix. The resulting value does not change after scrambling given that only the row of the original 2DPCA is scrambled in the process. The nearest neighbor classifier based on Manhattan distance is then employed to calculate the recognition rate
that is
the distance between the tested face images and all training samples
and the training sample category that corresponds to the minimum distance is taken as the category.
Result
2
The experimental results obtained for the four face databases reveal that after scrambling the original face and structural feature images by using a random binary matrix
the human eye cannot detect useful information
and the scrambled results can be regenerated. Therefore
scrambling the random binary matrix can ensure the security of the proposed algorithm. Compared with three other algorithms
the fusion of a structural feature can effectively improve the recognition rate. Among the three structural features considered in this work
the variance feature obtains the highest recognition rate
which has increased by 4.9% on the Georgia Tech(GT) database
2.25% on the Near Infrared(NIR) database
2.25% on the Visible Light(VIS) database
and 1.98% on the YouTube MakeupYMU database. The employed random binary matrix does not affect the recognition rate
that is
the recognition rates of each database are the same before and after random scrambling. Given that the introduced random matrix is binary
the values do not change after random scrambling. The average testing time for the four face databases is within 1 millisecond.
Conclusion
2
A combination of the original face image with the structural features of the human face enriches the representation ability of face image information and helps improve facial recognition rate. A random permutation operation can also protect the privacy of the original face image. When the biometric template is leaked
resetting position 1 in the random binary matrix will re-scramble the complex face matrix and generate a new biometric template. The proposed algorithm also shows an excellent real-time performance and can meet the demands of practical application scenarios.
Barni M, Droandi G and Lazzeretti R. 2015. Privacy protection in biometric-based recognition systems:a marriage between cryptography and signal processing. IEEE Signal Processing Magazine, 32(5):66-76[DOI: 10.1109/MSP.2015.2438131]
Cai R. 2018. Application of face recognition technology based on CA algorithm in intelligent residential property management. Journal of Intelligent and Fuzzy Systems, 35(3):2909-2919[DOI: 10.3233/JIFS-169646]
Chen C J, Dantcheva A and Ross A. 2013. Automatic facial makeup detection with application in face recognition//Proceedings of 2013 International Conference on Biometrics. Madrid, Spain: IEEE: 1-8[ DOI: 10.1109/ICB.2013.6612994 http://dx.doi.org/10.1109/ICB.2013.6612994 ]
Dantcheva A, Chen C J and Ross A. 2012. Can facial cosmetics affect the matching accuracy of face recognition systems?//Proceedings of the 5th IEEE International Conference on Biometrics: Theory, Applications, and Systems, USA. Arlington: IEEE: 391-398[ DOI: 10.1109/BTAS.2012.6374605 http://dx.doi.org/10.1109/BTAS.2012.6374605 ]
Dantcheva A, Elia P and Ross A. 2016. What else does your biometric data reveal? A survey on soft biometrics. IEEE Transactions on Information Forensics and Security, 11(3):441-467[DOI: 10.1109/TIFS.2015.2480381]
Han Q, Wu Z Y, Deng S Q, Qiao Z Q, Huang J J, Zhou J J and Liu J. 2018. Research on face recognition method by autoassociative memory based on RNNs. Complexity, 2018:#8524825[DOI: 10.1155/2018/8524825]
Jiang R, Bouridane A, Crookes D, Celebi M E and Wei H L. 2016. Privacy-protected facial biometric verification using fuzzy forest learning. IEEE Transactions on Fuzzy Systems, 24(4):779-790[DOI: 10.1109/TFUZZ.2015.2486803]
Jiang R, Ho A T S, Cheheb I, Al-Maadeed N, Al-Maadeed S and Bouridane A. 2017. Emotion recognition from scrambled facial images via many graph embedding. Pattern Recognition, 67:245-251[DOI: 10.1016/j.patcog.2017.02.003]
Kumar J A, Chalamala S and Kumar J S. 2018b. Face template protection using deep convolutional neural network//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE: 462-470[ DOI: 10.1109/cvprw.2018.00087 http://dx.doi.org/10.1109/cvprw.2018.00087 ]
Kaur H and Khanna P. 2017. Cancelable features using log-Gabor filters for biometric authentication. Multimedia Tools and Applications, 76(4):4673-4694[DOI: 10.1007/s11042-016-3652-3]
Kaur H and Khanna P. 2019. Random Slope method for generation of cancelable biometric features. Pattern Recognition Letters, 126:31-40[DOI: 10.1016/j.patrec.2018.02.016]
Kumar N and Rawat M. 2020. RP-LPP:a random permutation based locality preserving projection for cancelable biometric recognition. Multimedia Tools and Applications, 79(3):2363-2381[DOI: 10.1007/s11042-019-08228-2]
Kumar N, Singh S and Kumar A. 2018a. Random permutation principal component analysis for cancelable biometric recognition. Applied Intelligence, 48(9):2824-2836[DOI: 10.1007/s10489-017-1117-7]
Natgunanathan I, Mehmood A, Xiang Y, Hua G, Li G andBangay S. 2018. An overview of protection of privacy in multibiometrics. Multimedia Tools and Applications, 77(6):6753-6773[DOI: 10.1007/s11042-017-4596-y]
Rahulamathavan Y, Phan R C W, Chambers J A and Parish D J. 2012. Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Transactions on Affective Computing, 4(1):83-92[DOI: 10.1109/T-AFFC.2012.33]
Ratha N K, Connell J H and Bolle R M. 2001. Enhancing security and privacy in biometrics-based authentication system. IBM Systems Journal, 40(3):614-634[DOI: 10.1147/sj.403.0614]
Soliman R F, Amin M and El-Samie F E A. 2018a. A double random phase encoding approach for cancelable iris recognition. Optical and Quantum Electronics, 50(8):#326[DOI: 10.1007/s11082-018-1591-0]
Soliman R F, El Banby G M, Algarni A D, Elsheikh M, Soliman N F, Amin M and Abd El-Samie F E. 2018b. Double random phase encoding for cancelable face and iris recognition. Applied Optics, 57(35):10305-10316[DOI: 10.1364/AO.57.010305]
Taheri M, Mozaffari S and Keshavarzi P. 2015. Cancelable face verification using optical encryption and authentication. Journal of the Optical Society of America A, 32(10):1772-1779[DOI: 10.1364/JOSAA.32.001772]
Teoh A B J, Cho S and Kim J. 2018. Random permutation Maxout transform for cancellable facial template protection. Multimedia Tools and Applications, 77(21):27733-27759[DOI: 10.1007/s11042-018-5956-y]
Yang J, Zhang D, Frangi A F and Yang J Y. 2004. Two-dimensional PCA:a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1):131-137[DOI: 10.1109/TPAMI.2004.1261097]
Zhang J W, Shen W and Wu Z D. 2019. Recognition of face privacy protection using convolutional neural networks. Journal of Image and Graphics, 24(5):744-752
章坚武, 沈炜, 吴震东. 2019.卷积神经网络的人脸隐私保护识别.中国图象图形学报, 24(5):744-752[DOI: 10.11834/jig.180444]
相关文章
相关作者
相关机构
京公网安备11010802024621
