C3AE: Exploring the Limits of Compact Model for Age Estimation

Zhang, Chao; Liu, Shuaicheng; Xu, Xun; Zhu, Ce

doi:10.1109/cvpr.2019.01287

Cited by 76 publications

(81 citation statements)

References 42 publications

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“…To validate the performance of our proposed regression-and-ranking-estimator method on age estimation, we compared it with four representative age-estimation methods including GA-DFL [ 9 ], DOEL [ 22 ], C3AE [ 36 ], and CNN2ELM [ 15 ]. To make a fair comparison with the state-of-the-art methods, we adopted the same experimental setting as the work in [ 1 ].…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…For example, Rothe et al [ 1 ] first used the expected value on the softmax probabilities and then calculated the regression age. Zhang et al [ 36 ] presented the age representation as a distribution over two discrete adjacent bins. To better exploit the ordinal relationship among age labels, a few ranking-based methods were proposed recently.…”

Section: Related Workmentioning

confidence: 99%

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A Multifeature Learning and Fusion Network for Facial Age Estimation

Deng

Teng

Zhang

et al. 2021

Sensors

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Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Multifeature Learning and Fusion Network for Facial Age Estimation

Deng

Teng

Zhang

et al. 2021

Sensors

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show abstract

“…AGEn (IMDB-WIKI) 2.52 ARN [43] 3.00 ODFL-ODL [44] 3.12 C3AE [45] 2.78 C3AE (IMDB-WIKI) 2.75 MA-SFV2 2.68…”

Section: Methods Maementioning

confidence: 99%

Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network

et al. 2020

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Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.

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“…We compare our results with the classic and state-of-the-art age estimation methods, such as Deep Expectation (DEX) [24], Ordinal Hyperplanes Ranker (OHRank) [49], AGES [31], AGE group-n encoding (AGEn) [57], OR-CNN [58], and Compact yet efficient Cascade Context-based Age Estimation (C3AE) [78] on the Morph dataset as shown in Table 5. As per the comparison table, the proposed method has a beneficial impact in estimating the age of a person over the same dataset and demonstrates better results than the traditional as well as deep learning-based age estimation models.…”

Section: Age Estimationmentioning

confidence: 99%

Deep Learning Based Real Age and Gender Estimation from Unconstrained Face Image towards Smart Store Customer Relationship Management

Islam

Baek

2021

Applied Sciences

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The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation; similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer’s face image taken in an unconstrained environment to facilitate the smart store’s extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation.

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C3AE: Exploring the Limits of Compact Model for Age Estimation

Cited by 76 publications

References 42 publications

A Multifeature Learning and Fusion Network for Facial Age Estimation

A Multifeature Learning and Fusion Network for Facial Age Estimation

Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network

Deep Learning Based Real Age and Gender Estimation from Unconstrained Face Image towards Smart Store Customer Relationship Management

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