Fine-Grained Age Group Classification in the wild

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 under unconstrained conditions are relatively low for existing methods, we propose a method based on Attention LSTM network for Fine-Grained age group classification in the wild based on the idea of Fine-Grained categories and visual attention. This method combines ResNets models with LSTM unit to construct AL-ResNets networks to extract age-sensitive local regions, which effectively improves age estimation accuracy. Firstly, ResNets model pre-trained on ImageNet data set is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 data set for age estimation. Then, we fine-tune ResNets on the Adience data set 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, by combining the global and local features, we got our final prediction results. Our experiments illustrate the effectiveness of AL-ResNets for age group classification in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience data set.

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