Age Estimation Using Expectation of Label Distribution Learning

Gao, Bin-Bin; Zhou, Hongyu; Wu, Jianxin; Geng, Xin

doi:10.24963/ijcai.2018/99

Cited by 124 publications

(95 citation statements)

References 18 publications

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“…In both algorithms of T6, Bidirectional Long Short-Term Memory (BLSTM) architectures were explored with slightly varied parameters, loss functions, and summation methods. For example, Alg.1 employed Label Distribution Age Encoding (LDAE) [67] as age loss function, whereas, the 2nd algorithm exploited Deep Label Distribution Learning (DLDL) [68,69] as age loss function. They implemented two different accumulation methods: for Alg.1, test predictions were accumulated with arithmetic mean, and for Alg.2, the same was done with geometric mean.…”

Section: Explored Classifiersmentioning

confidence: 99%

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ahad

Ngo

Antar

et al. 2020

Sensors

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Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

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Section: Explored Classifiersmentioning

confidence: 99%

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ahad

Ngo

Antar

et al. 2020

Sensors

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“…The apparent age model was addressed as a classification problem that considers the age value as an independence category. The authors in [33] designed a lightweight CNN architecture that collectively learned age distribution and regressed it. The CNN-based approach, ThinAgeNet, employed the compression rate of 0.5.…”

Section: B Apparent Agementioning

confidence: 99%

“…In designing our CNN, we consider the model size and efficiency. The network is a modification of the CNN architecture in [36] [33].…”

Section: B Cnn Architecturementioning

confidence: 99%

A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images

Agbo-Ajala

Viriri

2020

IEEE Access

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Real and apparent age estimation of human face has attracted increased attention due to its numerous real-world applications. Different intelligent application scenarios can benefit from these computer-based systems that predict the ages of people correctly. Automatic apparent age system is particularly useful in medical diagnosis, facial beauty product development, movie role casting, the effect of plastic surgery, and anti-aging treatment. Predicting the real and apparent age of people has been quite difficult for both machines and humans. More recently, Deep learning with Convolutional Neural Networks (CNNs) methods have been extensively used for these classification task. It has incomparable advantages in extracting discriminative image features from human faces. However, many of the existing CNN-based methods are designed to be deeper and larger with more complex layers that makes it challenging to deploy on mobile devices with resource-constrained features. Therefore, we design a lightweight CNN model of fewer layers to estimate the real and apparent age of individuals from unconstrained real-time face images that can be deployed on mobile devices. The experimental results, when analyzed for classification accuracy on FG-NET, MORPH-II and APPA-REAL, with large-scale face images containing both real and apparent age annotations, show that our model obtains a state-of-the-art performance in both real and apparent age classification when compared to state-of-the-art methods. The new results and model size, therefore, confirm the usefulness of the model on resource-constrained mobile devices.INDEX TERMS apparent age, unconstrained images, convolutional neural network, mobile devices.

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“…Further, the author uses DLDL with KL testing the work on Chaleran and MORPH data set. The output gained on MAE is 5.75 [35] The backward face aging model digitally, means taking adult face and estimating the corresponding childhood face by considering face contour and its different components, face texture with the help of geometrical model. Consider the fact that face aging is non-linear process,…”

Section: Related Workmentioning

confidence: 99%

Age Estimation and its Progression from Face Images

Sharma¹,

Sharma²,

Jindal³

2019

IJRTE

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Face model improves the performance of evaluating the accurate age estimation with facial images and has enormous real-world applications. Human aging is a process of growing gradually old and mature. However, it is slow, depends upon person to person and most important it is irreversible. This paper mainly focuses on the various face model techniques, their performance metrics, databases, age estimation challenges to provide the researcher a great knowledge with recent journals in this field. Age estimation process progress with two modules: first part is feature extraction from the image and second module is age estimation. The accuracy or the desired output from age estimation model largely depends upon the features extraction, which if selected appropriately helps to achieve better results for research work.

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Age Estimation Using Expectation of Label Distribution Learning

Cited by 124 publications

References 18 publications

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images

Age Estimation and its Progression from Face Images

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