Deep Aging Face Verification With Large Gaps

Liu, Luoqi; Xiong, Chao; Zhang, Hanwang; Niu, Zhiheng; Wang, Meng; Yan, Shuicheng

doi:10.1109/tmm.2015.2500730

Cited by 55 publications

(29 citation statements)

References 26 publications

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“…Recently, some deep learning methods, including [33,[40][41][42][43][44], and data driven approaches, including [45,46], have been proposed for face recognition. In [33], coupled autoencoder networks have been used to recognize and retrieve face images with temporal variations.…”

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

“…In [33], coupled autoencoder networks have been used to recognize and retrieve face images with temporal variations. Similarly, [40][41][42][43][44] propose different neural networks to achieve age-invariant face recognition. Data-driven methods presented in [45,46] are based on cross-aging reference sets to perform face recognition across aging.…”

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

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Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts-demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations.

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Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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“…Another reason of face appearance's changes could be engendered by the ageing of the human face, and could impact on the entire AFR process if the time between each image capture is significant [18], as illustrated in Figure 5. To overcome face ageing issue in AFR, methods need to take properly into account facial ageing patterns [18].…”

Section: Ageing Of the Facementioning

confidence: 99%

“…To overcome face ageing issue in AFR, methods need to take properly into account facial ageing patterns [18]. Indeed, over time, not only face characteristics such as its shape or lines are modified [19], but other aspects are changing as well, e.g.…”

Section: Ageing Of the Facementioning

confidence: 99%

“…CCTV sequences or online movies. Indeed, on the one hand, the different variations in facial micro/macro expressions [100], which generate fast, facial dynamics and the different processes such as ageing, which is an extremely slow, dynamic problem since the face evolves over large periods of time [18], have all an impact on AFR techniques. On the other hand, face acquisition in videos intrinsically creates facial dynamics due to camera motion, change of point of view, as well as head's movements or pose variations.…”

Section: Dynamic Facementioning

confidence: 99%

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Automated Face Recognition: Challenges and Solutions

Olszewska¹

2016

Pattern Recognition - Analysis and Applications

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Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real-world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods.

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Age-Puzzle FaceNet for Cross-Age Face Recognition

Huang

Chen

2019

Computer Vision – ACCV 2018

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Deep Aging Face Verification With Large Gaps

Cited by 55 publications

References 26 publications

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Automated Face Recognition: Challenges and Solutions

Age-Puzzle FaceNet for Cross-Age Face Recognition

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