Age invariant face recognition and retrieval by coupled auto-encoder networks

Xu, Chen; Liu, Qihe; Ye, Mao

doi:10.1016/j.neucom.2016.10.010

Cited by 104 publications

(56 citation statements)

References 42 publications

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“…Method Acc (%) CAN [52] 92.30 VGGFace [30] 96.00 Center Loss [49] 97.48 MFM-CNN [50] 97.95 LF-CNN [48] 98.50 Marginal Loss [11] 98.95 DeepVisage [16] 99.13 OE-CNN [45] 99.20 Human, avg. [6] 85.70 Human, voting [6] 94.20 AIM (Ours) 99.38 AIM + CAFR (Ours) 99.76…”

Section: Vsmentioning

confidence: 99%

Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

Zhao

Cheng

et al. 2019

AAAI

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Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intra-class variations. As opposed to current techniques for age-invariant face recognition, which either directly extract age-invariant features for recognition, or first synthesize a face that matches target age before feature extraction, we argue that it is more desirable to perform both tasks jointly so that they can leverage each other. To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties. First, AIM presents a novel unified deep architecture jointly performing cross-age face synthesis and recognition in a mutual boosting way. Second, AIM achieves continuous face rejuvenation/aging with remarkable photorealistic and identitypreserving properties, avoiding the requirement of paired data and the true age of testing samples. Third, we develop effective and novel training strategies for end-to-end learning the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the age variation. Moreover, we propose a new large-scale Cross-Age Face Recognition (CAFR) benchmark dataset to facilitate existing efforts and push the frontiers of age-invariant face recognition research. Extensive experiments on both our CAFR and several other cross-age datasets (MORPH, CACD and FG-NET) demonstrate the superiority of the proposed AIM model over the state-of-thearts. Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recogniz- *

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Section: Vsmentioning

confidence: 99%

Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

Zhao

Cheng

et al. 2019

AAAI

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“…There exists a sizable amount of literature on recognition of age-separated face images, such as [24][25][26][27][28][29][30][31][32][33][34][35][36]. The existing methods can be categorized as generative or discriminative.…”

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

“…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.…”

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

“…Following existing face-retrieval methods [33,53,54] for a given a set of p query face images, Q = y 1 q , y 2 q , . .…”

Section: Face Retrieval Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

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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Biometrics for Surveillance

Yan¹

2019

Texts in Computer Science

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Age invariant face recognition and retrieval by coupled auto-encoder networks

Cited by 104 publications

References 42 publications

Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Biometrics for Surveillance

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