Age group classification in the wild with deep RoR architecture

Zhang, Alice; Guo, Baicun; Gao, Ce; Zhao, Dongyao; Sun, Edward; Yuan, Feng

doi:10.1109/icip.2017.8296549

Cited by 3 publications

(2 citation statements)

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“…RoR was constructed by adding identity shortcuts level-by-level based on original residual networks. It is noteworthy to mention that recently RoR also succeeded in the study of age estimation [7] [23] for its outstanding performance. Therefore, in this paper, we construct new network structures named AL-ResNets and AL-RoR, which are based on the ResNets and RoR models, with the notion that both network depths and residual blocks information are efficiently represented in the architecture description.…”

Section: A Network Architecturementioning

confidence: 99%

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Fine-Grained Age Estimation in the Wild With Attention LSTM Networks

Zhang

Liu

Yuan

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision which has a wide range of practical application value. Concerning the problem that accuracy of age estimation of face images in the wild are relatively low for existing methods, where they take into account only the whole features of face image while neglecting the fine-grained features of age-sensitive area, we propose a method based on Attention LSTM network for Fine-Grained age estimation in the wild based on the idea of Fine-Grained categories and visual attention mechanism. This method combines ResNets or RoR models with LSTM unit to construct AL-ResNets or AL-RoR networks to extract agesensitive local regions, which effectively improves age estimation accuracy. Firstly, ResNets or RoR model pre-trained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we finetune ResNets or RoR on the target age datasets to extract the global features of face images. To extract the local characteristics of age-sensitive areas, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, the age group classification experiment is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and LAP datasets. By combining the global and local features, we got our final prediction results. Our experiments illustrate the effectiveness of AL-ResNets or AL-RoR for age estimation in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience, MORPH Album 2, FG-NET and LAP datasets.

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Section: A Network Architecturementioning

confidence: 99%

“…-error is mainly affected by mean µ and standard deviation δ, as well as network prediction output value, where they are subject to a normal distribution. The expression of -error is shown in (7), where x j , µ j , δ j are the predicted age, the apparent age value and the standard deviation, respectively, and N is the number of all test images.…”

Section: Lapmentioning

confidence: 99%