Conditional adversarial consistent identity autoencoder for cross-age face synthesis

Bian, Xiaohang; Li, Jianwu

doi:10.1007/s11042-020-10442-2

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…Image Pre-processing. UTK-Face and FG-Net datasets [16] were selected for experiments. The UTK-Face data set contains 23708 face images with age and gender annotation.…”

Section: Methodsmentioning

confidence: 99%

Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)

Li¹,

Li²,

Jiang³

et al. 2023

Journal of Compurters

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<p>In recent years, the research of face age features has achieved rapid development driven by deep learning. The faces generated by the Conditional Adversarial Auto-encoder (CAAE) model are not only highly credible, but also closer to the target age. However, there are many problems, such as low resolution of human face image generation and poor local feature retention effect of human face features. To this end, this paper improves on the CAAE network. Firstly, referring to the LSGAN network structure, the 4 convolution layers of the encoder are added to 5 layers and the 4 convolution layers of the generator are added to 7 layers. Secondly, on the basis of the original loss function, the image gradient difference loss function is added to ensure the output face image quality. Meanwhile, the data set were preprocessed for face correction. Finally, this paper performs face similarity analysis on the Eye-key platform and contrasts the generated image quality using structural similarity and peak signal to noise ratio metrics. In addition, the generated results were tested for their robustness. The experimental results show that the average similarity of faces generated by the Improved Conditional Adversarial Auto-encoder (I-CAAE) network was increased by 3.9. And the average peak signal to noise ratio of the generated pictures was reduced by 1.8. Confirming the superiority of the proposed method.</p> <p> </p>

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“…Image Pre-processing. UTK-Face and FG-Net datasets [16] were selected for experiments. The UTK-Face data set contains 23708 face images with age and gender annotation.…”

Section: Methodsmentioning

confidence: 99%

Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)

Li¹,

Li²,

Jiang³

et al. 2023

Journal of Compurters

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“…In this paper, experiments are conducted on the AFAD data set [11] and the FG-NET data set [12].The FG-NET data set created by collecting selfies on specific social networks.Contains more than 160,000 Asian face images in the age range of 15-70 years old and the corresponding age and gender labels. The Asian face data set is used to better fit the Chinese face features, therefore, the face data set was used for age estimation.…”

Section: Data Setmentioning

confidence: 99%

Face age estimation network based on improved residual blocks

Li¹,

Jiang²,

Yi³

et al. 2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

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“…The total number of rejections of the null involves both the number of FP and TP [181]. FDR can be simply computed as follows: FDR = FP/(FP + TP), (11) 11. Geometric mean (G-Mean): Estimates the balance between classification performances on both the majority and minority classes.…”

Section: Masked Face Recognitionmentioning

confidence: 99%

“…However, research efforts had been under way, even before the COVID-19 pandemic, on how deep learning could improve the performance of existing recognition systems with the existence of masks or occlusions. For instance, the task of occluded face recognition (OFR) has attracted extensive attention, and many deep learning methods have been proposed, including sparse representations [9,10], autoencoders [11], video-based object tracking [12], bidirectional deep networks [13], and dictionary learning [14].…”

Section: Introductionmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

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Conditional adversarial consistent identity autoencoder for cross-age face synthesis

Cited by 7 publications

References 23 publications

Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)

Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)

Face age estimation network based on improved residual blocks

Masked Face Recognition Using Deep Learning: A Review

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