亲缘关系约束下的青少年人脸年龄合成研究

doi:10.3778/j.issn.1002-8331.2207-0449

摘要/Abstract

摘要： 年龄合成生成逼真的人脸图像可以有效地提高跨年龄人脸验证准确率，对寻找走失人口有着重要的意义，但是青少年颅骨复合体未发育完全，使得面向青少年的年龄合成十分困难。因此提出了一种面向青少年的端到端的年龄合成模型。通过StyleGAN保留人脸的语义信息，在人脸编码特征上添加年龄通道实现年龄的转化，引入亲缘特征匹配模块引导青少年的面部老化，将亲缘特征匹配率加入损失函数参与训练。该算法模型可以在保持个体身份信息的同时，实现平滑的年龄合成，生成逼真的人脸图像。该模型不仅提升了视觉效果，并且实验表明该模型跨年龄人脸验证准确率达到95.3%，身份召回率达到92.7%，年龄合成平均年龄误差减少4年，较现有算法有较好的提升。

关键词: 年龄合成, StyleGAN, 亲缘特征匹配, 跨年龄人脸验证

Abstract: Generation of realistic face images by age synthesis can effectively improve the accuracy of cross-age face verification, which is of great significance for finding the missing population. However, the immature skull complex of adolescents makes it very difficult to age synthesis. Therefore, an end-to-end age synthesis model for adolescents is proposed. The semantic information of the face is preserved through StyleGAN, the age channel is added to the face coding feature to realize the age conversion, the affinity feature matching module is introduced to guide the adolescents’ facial aging, and the affinity feature matching rate is added to the loss function in the training. The algorithm model can achieve smooth age synthesis and generate realistic face images while maintaining individual identity information, which not only improves the visual effect, but also shows that the accuracy of cross-age face verification reaches 95.3%, the identity recall rate reaches 92.7%, and the average age error of age synthesis decreases by 4 years, which is better than the existing algorithms.

Key words: age synthesis, StyleGAN, kinship verification, cross-age face verification

林霞, 李建微. 亲缘关系约束下的青少年人脸年龄合成研究[J]. 计算机工程与应用, 2023, 59(22): 166-173.

LIN Xia, LI Jianwei. Research on Age Synthesis of Adolescents Under Constraints of Kinship[J]. Computer Engineering and Applications, 2023, 59(22): 166-173.

参考文献

[1] GRIMMER M，RAMACHANDRA R，BUSCH C.Deep face age progression：a survey[J].IEEE Access，2021，9：83376-83393.
[2] HUANG Z L，WENG W G.Analysis on geographical migration networks of child trafficking crime for illegal adoption from 2008 to 2017 in China[J].Physica A：Statistical Mechanics and its Applications，2019，528：121404.
[3] DEB D，AGGARWAL D，JAIN A K.Finding missing children：aging deep face features[J].arXiv：1911.07538，2019.
[4] DEB D，NAIN N，JAIN A K.Longitudinal study of child face recognition[C]//Proceedings of the 11th IAPR International Conference on Biometrics，2018：225-232.
[5] HAN J H.Prediction of changed faces with HSCNN[J].Computers Materials & Continua，2022，71（2）：3747-3759.
[6] WANG H，SANCHEZ V，LI C T.Age-oriented face synthesis with conditional discriminator pool and adversarial triplet loss[J].IEEE Transactions on Image Processing，2021，30：5413-5425.
[7] SONG J，ZHANG J，GAO L，et al.AgeGAN++：face aging and rejuvenation with dual conditional GANs[J].IEEE Transactions on Multimedia，2021，24：791-804.
[8] LIU Y，LI Q，SUN Z，et al.a3GAN：an attribute-aware attentive generative adversarial network for face aging[J].IEEE Transactions on Information Forensics and Security，2021，16：2776-2790.
[9] HUANG Z，CHEN S，ZHANG J，et al.PFA-GAN：progressive face aging with generative adversarial network[J].IEEE Transactions on Information Forensics and Security，2021，16：2031-2045.
[10] BIAN X H，LI J W.Conditional adversarial consistent identity autoencoder for cross-age face synthesis[J].Multimedia Tools and Applications，2021，80（9）：14231-14253.
[11] ALALUF Y，PATASHNIK O，COHEN-OR D.Only a matter of style：age transformation using a style-based regression model[J].ACM Transactions on Graphics，2021，40（4）：45.
[12] SRINIVAS N，RICANEK K，MICHALSKI D，et al.Face recognition algorithm bias：performance differences on images of children and adults[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops，2019：2269-2277.
[13] RICANEK K，BHARDWAJ S，SODOMSKY M.A review of face recognition against longitudinal child faces[C]//Proceedings of the 14th International Conference of the Biometrics Special Interest Group，2015：15-26.
[14] QIN X Q，TAN X Y，CHEN S C.Tri-subject kinship verification：understanding the core of a family[J].IEEE Transactions on Multimedia，2015，17（10）：1855-1867.
[15] ANTIPOV G，BACCOUCHE M，DUGELAY J L.Face aging with conditional generative adversarial networks[C]//Proceedings of the 2017 IEEE International Conference on Image Processing，2017：2089-2093.
[16] ZHANG Z，SONG Y，QI H.Age progression/regression by conditional adversarial autoencoder[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition，2017：4352-4360.
[17] YANG H Y，HUANG D，WANG Y H，et al.Learning face age progression：a pyramid architecture of GANs[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2018：31-39.
[18] LI P，HU Y，LI Q，et al.Global and local consistent age generative adversarial networks[C]//Proceedings of the 2018 24th International Conference on Pattern Recognition，2018：1073-1078.
[19] TANG X，WANG Z，LUO W，et al.Face aging with identity-preserved conditional generative adversarial networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2018：7939-7947.
[20] ZHAO J，CHENG Y，CHENG Y，et al.Look across elapse：disentangled representation learning and photorealistic cross-age face synthesis for age-invariant face recognition[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence，2019：9251-9258.
[21] ZHAO J，YAN S，FENG J.Towards age-invariant face recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，44（1）：474-487.
[22] HUANG Z，ZHANG J，SHAN H.When age-invariant face recognition meets face age synthesis：a multi-task learning framework[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：7278-7287.
[23] DEB D，AGGARWAL D，JAIN A K.Identifying missing children：face age-progression via deep feature aging[C]//Proceedings of the 2020 25th International Conference on Pattern Recognition，2021：10540-10547.
[24] CHANDALIYA P K，NAIN N.ChildGAN：face aging and rejuvenation to find missing children[J].Pattern Recognition，2022，129：108761.
[25] ZHANG H，GOODFELLOW L，METAXAS D，et al.Self-attention generative adversarial networks[C]//Proceedings of the 36th International Conference on Machine Learning，2019：7354-7363.
[26] LIU L，YU H B，WANG S H，et al.Learning shape and texture progression for young child face aging[J].Signal Processing：Image Communication，2021，93：116127.
[27] SHU X B，TANG J H，LAI H J，et al.Kinship-guided age progression[J].Pattern Recognition，2016，59：156-167.
[28] ZHANG K H，HUANG Y Z，SONG C F，et al.Kinship verification with deep convolutional neural networks[C]//Proceedings of the British Machine Vision Conference，2015：148.
[29] DEHGHAN A，ORTIZ E G，VILLEGAS R，et al.Who do I look like? Determining parent-offspring resemblance via gated autoencoders[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition，2014：1757-1764.
[30] ZHANG P P，WANG D，LU H C，et al.Amulet：aggregating multi-level convolutional features for salient object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision，2017：202-211.
[31] ZHANG Q，HUANG N C，YAO L，et al.RGB-T salient object detection via fusing multi-level CNN features[J].IEEE Transactions on Image Processing，2019，29：3321-3335.
[32] YAN H B，SONG C H.Multi-scale deep relational reasoning for facial kinship verification[J].Pattern Recognition，2021，110：107541.
[33] LU J W，HU J L，TAN Y P.Discriminative deep metric learning for face and kinship verification[J].IEEE Transactions on Image Processing，2017，26（9）：4269-4282.
[34] ZHOU X J，JIN K，XU M，et al.Learning deep compact similarity metric for kinship verification from face images[J].Information Fusion，2019，48：84-94.
[35] NANDY A，MONDAL S S.Kinship verification using deep siamese convolutional neural network[C]//Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition，2019：739-743.
[36] ISOLA P，ZHU J Y，ZHOU T H，et al.Image-to-image translation with conditional adversarial networks[C]//Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition，2017：5967-5976.
[37] RICHARDSON E，ALALUF Y，PATASHNIK O，et al.Encoding in style：a stylegan encoder for image-to-image translation[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：2287-2296.
[38] SIMONYAN K，ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv：1409.
1556，2014.
[39] HE K M，ZHANG X Y，REN S Q，et al.Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[40] HUANG S，LIN J K，HUANGFU L W，et al.Adaptively weighted k-tuple metric network for kinship verification[J].IEEE Transactions on Cybernetics，2022.DOI：10.1109/TCYB.2022.3163707.
[41] DENG J K，GUO J，XUE N N，et al.ArcFace：additive angular margin loss for deep face recognition[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：4685-4694.
[42] GEORGOPOULOS M，PANAGAKIS Y，PANTIC M.Modeling of facial aging and kinship：a survey[J].Image and Vision Computing，2018，80：58-79.
[43] SCHROFF F，KALENICHENKO D，PHILBIN J，et al.FaceNet：a unified embedding for face recognition and clustering[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition，2015：815-823.