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

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## Abstract

**:**

## 1. Introduction

- (1)
- A lightweight convolutional neural network age estimation model based on the mixed attention mechanism is constructed.
- (2)
- The age estimation method combining classification and regression is easy to implement and the final age estimation accuracy is very high.
- (3)
- Perform face detection and correction on the input face image, and perform image augmentation, so that the feature information related to the face age is amplified, which is helpful for network learning.

## 2. Related Work

#### 2.1. Feature Extraction

#### 2.2. Age Estimation

## 3. Proposed Method

#### 3.1. Architecture

#### 3.2. Mixed Attention-ShuffleNetV2

#### 3.3. Data Augmentation

#### 3.4. Loss Function

- 1.
- Classification loss

- 2.
- Regression loss

## 4. Experiments

#### 4.1. Datasets

#### 4.2. Evaluation Metrics

- 1.
- Mean absolute error (MAE)MAE is defined as the average of the absolute deviations of all age estimates and true values.
- 2.
- Cumulative Score (CS)

#### 4.3. Parameter Setting

#### 4.4. Multiple Sets of Experiments

#### 4.4.1. Experiment on MORPH2

#### 4.4.2. Experiment on FG-NET (Face and Gesture Recognition Research Network Aging Database)

#### 4.5. Quantitative Comparison with Other Methods

#### 4.5.1. Comparison on MORPH2

#### 4.5.2. Comparison on FG-NET

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 3.**The basic unit of ShuffleNetV2. (

**a**) The basic unit of original ShuffleNetV2. (

**b**) The basic unit of our ShuffleNetV2 with mixed attention mechanism added.

**Figure 8.**Some examples in the FG-NET (Face and Gesture Recognition Research Network Aging Database) dataset.

**Figure 10.**Final LOSS (

**a**) and MAE (

**b**) (Mean Absolute Error) curves without pre-processing data using image augmentation.

Layer | Output Size | KSize | Stride | Repeat | Output Channels |
---|---|---|---|---|---|

Image | 224 × 224 | 3 | |||

Conv1 MaxPool | 112 × 112 | 3 × 3 | 2 | 1 | 24 |

Stage2 | 28 × 28 | 1 | 3 | 244 | |

Stage3 | 14 × 14 | 1 | 7 | 488 | |

Stage4 | 7 × 7 | 1 | 3 | 976 | |

Conv5 | 7 × 7 | 1 × 1 | 2048 | ||

GlobalPool | 1 × 1 | 7 × 7 | |||

FC | 101 | ||||

LastFC | 1 |

**Table 2.**Comparison of MAE (Mean Absolute Error) on MORPH2 (Craniofacial Longitudinal Morphological Face Database).

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## Share and Cite

**MDPI and ACS Style**

Liu, X.; Zou, Y.; Kuang, H.; Ma, X.
Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network. *Symmetry* **2020**, *12*, 146.
https://doi.org/10.3390/sym12010146

**AMA Style**

Liu X, Zou Y, Kuang H, Ma X.
Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network. *Symmetry*. 2020; 12(1):146.
https://doi.org/10.3390/sym12010146

**Chicago/Turabian Style**

Liu, Xinhua, Yao Zou, Hailan Kuang, and Xiaolin Ma.
2020. "Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network" *Symmetry* 12, no. 1: 146.
https://doi.org/10.3390/sym12010146