Efficient Group-n Encoding and Decoding for Facial Age Estimation

Tan, Zichang; Wan, Jun; Lei, Zhen; Zhi, Ruicong; Guo, Guodong; Li, Stan Z.

doi:10.1109/tpami.2017.2779808

Cited by 97 publications

(53 citation statements)

References 32 publications

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“…AGES [11] 8.83 OR-CNN [19] 3.27 DEX [32] 3.25 DEX (IMDB-WIKI) 2.68 OHRank [33] 6.07 CS-LR [38] 4.59 Sparsity [39] 3.45 GA-DFL [40] 3.37 AD [41] 2.78 AGEn [42] 2.93 AGEn (IMDB-WIKI)…”

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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“…AGES [11] 8.83 OR-CNN [19] 3.27 DEX [32] 3.25 DEX (IMDB-WIKI) 2.68 OHRank [33] 6.07 CS-LR [38] 4.59 Sparsity [39] 3.45 GA-DFL [40] 3.37 AD [41] 2.78 AGEn [42] 2.93 AGEn (IMDB-WIKI)…”

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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“…With the optimal age grouping in classification branch, weight setting of two tasks and shared layers of two tasks, We compare our CR-MT net with some representative age estimation methods of recent years, where NET-Hybrid [27] and AGEn [39] are deep MTL methods, the others are divideand-conquer methods. The comparative results from three datasets are illustrated in Table 7, Table 8, and Table 9 respectively.…”

Section: F Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Both Niu et al [14] and Tan et al [39] transform the age ordinal regression problem into a series of binary classification sub-problems, and use a multiple output CNN to solve these sub-problems, where each output layer corresponds to a binary classification task, and all the tasks share the same intermediate layers. The difference is that Tan et al [39] encodes the relationship among adjacent ages in age grouping, i.e. adjacent ages are grouped into the same group and each age corresponds to n groups.…”

Section: Related Workmentioning

confidence: 99%

Facial Age Estimation Using a Multi-task Network Combining Classification and Regression

2020

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Age estimation of facial images is very challenging because of the complexity of face aging process and the difficulty of collecting and labeling data. A holistic regression model is subject to imbalanced training data, while a divide-and-conquer method highly depends on the effect of the age classification, which usually has boundary effect due to cross-age correlations. This paper proposes a simple but effective multi-task learning (MTL) network combining classification and regression for age estimation called CR-MT net, where classification acts as an auxiliary task to regression. MTL can boost the generalization performance of the age regression task by shared information representation learning of the two tasks. Compared to divide-and-conquer methods, our method performs MTL for two tasks with an end-to-end training, and has no error propagation from classification to regression. The holistic regression model in CR-MT net does not meet the boundary effect, and can fit the heterogeneous or unbalanced age data more accurately with the aid of a good age data partition in classification. We evaluate two age grouping techniques to find a good data partition, and diagnose various factors which can influence the performance of the CR-MT net by extensive experiments. The CR-MT net is verified in three public datasets, and achieves state-of-art results. INDEX TERMS Age estimation, multi-task learning, deep learning.

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“…Reference [6] describes a fuzzy system for emotional intent classifying, while [29] describes the affect estimation by audio stream using ensemble of ordinal classifiers. Many works have been devoted to the recognition of emotions from photos and videos using deep CNN, for example [30,31].…”

Section: Related Workmentioning

confidence: 99%

The Extended Multidimensional Neo-Fuzzy System and Its Fast Learning in Pattern Recognition Tasks

Bodyanskiy

Kulishova

Chala

2018

Data

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Methods of machine learning and data mining are becoming the cornerstone in information technologies with real-time image and video recognition methods getting more and more attention.While computational system architectures are getting larger and more complex, their learning methods call for changes, as training datasets often reach tens and hundreds of thousands of samples, therefore increasing the learning time of such systems. It is possible to reduce computational costs by tuning the system structure to allow fast, high accuracy learning algorithms to be applied. This paper proposes a system based on extended multidimensional neo-fuzzy units and its learning algorithm designed for data streams processing tasks. The proposed learning algorithm, based on the information entropy criterion, has significantly improved the system approximating capabilities. Experiments have confirmed the efficiency of the proposed system in solving real-time video stream recognition tasks.

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Efficient Group-n Encoding and Decoding for Facial Age Estimation

Cited by 97 publications

References 32 publications

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

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

Facial Age Estimation Using a Multi-task Network Combining Classification and Regression

The Extended Multidimensional Neo-Fuzzy System and Its Fast Learning in Pattern Recognition Tasks

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