Facial aging and asymmetry decomposition based approaches to identification of twins

Le, Trung-Nghia; Seshadri, Keshav; Luu, Khoa; Savvides, Marios

doi:10.1016/j.patcog.2015.05.021

Cited by 20 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, Active Appearance Models (AAM) (Cootes et al, 2001) were adopted in several works to unify the facial shape and texture (i.e. wrinkles) features for age estimation (Lanitis et al, 2004;Luu et al, 2009a;Choi et al, 2011;Zhang and Yeung, 2010;Chang et al, 2011;Duong et al, 2011;Xu et al, 2011Xu et al, , 2015Le et al, 2015;Luu, 2010;Chen et al, 2011).…”

Section: Age Progressionmentioning

confidence: 99%

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper presents a novel Subject-dependent Deep Aging Path (SDAP), which inherits the merits of both Generative Probabilistic Modeling and Inverse Reinforcement Learning to model the facial structures and the longitudinal face aging process of a given subject. The proposed SDAP is optimized using tractable log-likelihood objective functions with Convolutional Neural Networks (CNNs) based deep feature extraction. Instead of applying a fixed aging development path for all input faces and subjects, SDAP is able to provide the most appropriate aging development path for individual subject that optimizes the reward aging formulation. Unlike previous methods that can take only one image as the input, SDAP further allows multiple images as inputs, i.e. all information of a subject at either the same or different ages, to produce the optimal aging path for the given subject. Finally, SDAP allows efficiently synthesizing in-thewild aging faces. The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification. The experimental results consistently show SDAP achieves the state-of-the-art performance on numerous face aging databases, i.e. FG-NET, MORPH, AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). Furthermore, we also evaluate the performance of SDAP on large-scale Megaface challenge to demonstrate the advantages of the proposed solution.

show abstract

Section: Age Progressionmentioning

confidence: 99%

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Supervised deep learning models have recently achieved numerous breakthrough results in various applications, for example, Image Classification [1][2][3], Object Detection [4][5][6], Face Recognition [7][8][9][10][11][12][13][14], Image Segmentation [15,16] and Generative Model [17][18][19][20][21][22]. However, these methods usually require a huge number of annotated data, which is highly expensive.…”

Section: Introductionmentioning

confidence: 99%

Fast Flow Reconstruction via Robust Invertible n × n Convolution

et al. 2021

Self Cite

View full text Add to dashboard Cite

Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first introduced a simple type of generative flow using an invertible 1×1 convolution. However, the 1×1 convolution suffers from limited flexibility compared to the standard convolutions. In this paper, we propose a novel invertible n×n convolution approach that overcomes the limitations of the invertible 1×1 convolution. In addition, our proposed network is not only tractable and invertible but also uses fewer parameters than standard convolutions. The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible n×n convolution helps to improve the performance of generative models significantly.

show abstract

“…technologies [25], there is no apparent technique to inverse that embedding process to reconstruct the faces of a subject given his/her extracted features from those engines. Some Blackbox Adversarial Attack approaches [20,21,44] have partially addressed this task by analyzing the gradients of the classifier's outputs to generate adversarial examples that mislead the behaviour of that classifier.…”

Section: Introductionmentioning

confidence: 99%

Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

Duong¹,

Truong²,

Quach³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Unveiling face images of a subject given his/her highlevel representations extracted from a blackbox Face Recognition engine is extremely challenging. It is because the limitations of accessible information from that engine including its structure and uninterpretable extracted features. This paper presents a novel generative structure with Bijective Metric Learning, namely Bijective Generative Adversarial Networks in a Distillation framework (DiBiGAN), for synthesizing faces of an identity given that person's features. In order to effectively address this problem, this work firstly introduces a bijective metric so that the distance measurement and metric learning process can be directly adopted in image domain for an image reconstruction task. Secondly, a distillation process is introduced to maximize the information exploited from the blackbox face recognition engine. Then a Feature-Conditional Generator Structure with Exponential Weighting Strategy is presented for a more robust generator that can synthesize realistic faces with ID preservation. Results on several benchmarking datasets including CelebA, LFW, AgeDB, CFP-FP against matching engines have demonstrated the effectiveness of DiBiGAN on both image realism and ID preservation properties.

show abstract

Facial aging and asymmetry decomposition based approaches to identification of twins

Cited by 20 publications

References 44 publications

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

Fast Flow Reconstruction via Robust Invertible n × n Convolution

Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

Contact Info

Product

Resources

About