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Deep learning approach for facial age classification: a survey of the state-of-the-art

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Abstract

Age estimation using face images is an exciting and challenging task. The traits from the face images are used to determine age, gender, ethnic background, and emotion of people. Among this set of traits, age estimation can be valuable in several potential real-time applications. The traditional hand-crafted methods relied-on for age estimation, cannot correctly estimate the age. The availability of huge datasets for training and an increase in computational power has made deep learning with convolutional neural network a better method for age estimation; convolutional neural network will learn discriminative feature descriptors directly from image pixels. Several convolutional neural net work approaches have been proposed by many of the researchers, and these have made a significant impact on the results and performances of age estimation systems. In this paper, we present a thorough study of the state-of-the-art deep learning techniques which estimate age from human faces. We discuss the popular convolutional neural network architectures used for age estimation, presents a critical analysis of the performance of some deep learning models on popular facial aging datasets, and study the standard evaluation metrics used for performance evaluations. Finally, we try to analyze the main aspects that can increase the performance of the age estimation system in future.

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Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Ad:

Adience

AFAD:

Asian Face Age Dataset

BERC:

Biometric Engineering Research Center

CACD:

Cross-Age Celebrity Dataset

CNN:

Convolutional Neural Network

CS:

Cummulative Score

DEX:

Deep EXpectation

DLDL:

Deep Label Distribution Learning

ELM:

Extreme Learning Machine

FC:

Fully Connected

FG:

FG-NET

FG-NET:

Face and Gesture Recognition Network

GA-DFL:

Group-aware Deep Feature Learning

HOIP:

Human Object Interaction Processing

IMW:

IMDb-WIKI

KL:

Kullback–Leibler

LAP2015:

Looking At People 2015

LAP2016:

Looking At People 2016

LHI:

Lotus Hill Research Institute

L5:

LAP2015

L6:

LAP2016

MAE:

Mean Absolute Error

MC:

Multi-class Classification

MP:

MORPH-II

MR:

Metric Regression

MRCNN:

Multi-Region Convolutional Neural Network

ODFL:

Ordinal Deep Feature Learning

OR-CNN:

Ordinal Regression Convolutional Neural Network

RoR:

Residual Networks of Residual Networks

VGGNET:

Visual Geometry Group Network

WIT-DB:

Waseda human–computer Interaction Technology

Xception:

Extreme Inception

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  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa

    Olatunbosun Agbo-Ajala & Serestina Viriri

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  1. Olatunbosun Agbo-Ajala
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Correspondence to Serestina Viriri.

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Agbo-Ajala, O., Viriri, S. Deep learning approach for facial age classification: a survey of the state-of-the-art. Artif Intell Rev 54, 179–213 (2021). https://doi.org/10.1007/s10462-020-09855-0

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