Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

Rothe, Rasmus; Timofte, Radu; Gool, Luc Van

doi:10.1007/s11263-016-0940-3

Cited by 614 publications

(495 citation statements)

References 55 publications

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“…In [31], convolutional neural networks are used for the extraction of deep features, then the standard support vector regression is used for gender and age prediction. Recently, the model in [31] is further improved by adding an expected value formulation after classification [32]. The directional age-primitive pattern (DAPP) is proposed in [33], which is a local face descriptor containing aging cue information; the model obtained state-of-theart performance on Adience dataset.…”

Section: Cnn For Age and Gendermentioning

confidence: 99%

Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

Zhang

2018

Journal of Sensors

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Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a "global" network. The age and gender classification results are obtained by combining both the outputs from both the "global" and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.

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Section: Cnn For Age and Gendermentioning

confidence: 99%

Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

Zhang

2018

Journal of Sensors

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“…In particular we evaluate different handcrafted and deep learned features on CelebA to then validate and report prediction results on both CelebA and Facebook BIG5 datasets. We used the off-the-shelf face detector of Mathias et al [34] for detection and alignment of the faces as done for the DEX Network in [11,12]. …”

Section: Attributementioning

confidence: 99%

“…For the deep features, we extract the fc6 and fc7 4096-dimensional layers of the DEX Network [11], which is a variant of the VGG-16 [37] trained for age estimation on hundreds of thousands of face images from IMDB-WIKI dataset [11,12]. Since most facial attributes are relevant signals for age estimation, these features work quite well off-the-shelf.…”

Section: Deep Featuresmentioning

confidence: 99%

“…Convolutional Neural Networks (CNNs) proved their effectiveness on many computer vision tasks such as classification and end-to-end mappings [8][9][10]. The same is valid for attribute prediction where the recent top solutions rely on deep learning for representations and/or for direct attribute prediction [1,6,[11][12][13][14]. We observe that generally the methods treat the prediction of each attribute individually starting from the face image even if there are clear dependencies between some attributes ('males' are unlikely to wear 'earrings' or 'lipsticks', 'females' to be 'bold' and have a 'goatee').…”

Section: Introductionmentioning

confidence: 99%

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From Face Images and Attributes to Attributes

Torfason

Agustsson

Rothe

et al. 2017

Computer Vision – ACCV 2016

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Abstract. The face is an important part of the identity of a person. Numerous applications benefit from the recent advances in prediction of face attributes, including biometrics (like age, gender, ethnicity) and accessories (eyeglasses, hat). We study the attributes' relations to other attributes and to face images and propose prediction models for them. We show that handcrafted features can be as good as deep features, that the attributes themselves are powerful enough to predict other attributes and that clustering the samples according to their attributes can mitigate the training complexity for deep learning. We set new state-of-the-art results on two of the largest datasets to date, CelebA and Facebook BIG5, by predicting attributes either from face images, from other attributes, or from both face and other attributes. Particularly, on Facebook dataset, we show that we can accurately predict personality traits (BIG5) from tens of 'likes' or from only a profile picture and a couple of 'likes' comparing positively to human reference.

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“…The Deep EXpectation (DEX) on apparent age method [22,23] uses the VGG-16 architecture for its networks (see Figure 14), which are pre-trained on ImageNet for image classification. In addition, the authors explored the benefit of fine-tuning over crawled Internet face images with available age.…”

Section: Dex-imdb-wiki and Dex-chalearn-iccv2015 Featuresmentioning

confidence: 99%

Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features

Belver

Arganda‐Carreras

Dornaika

2017

Video Analytics. Face and Facial Expression Recognition and Audience Measurement

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In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biometrics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation.In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

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Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

Cited by 614 publications

References 55 publications

Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

From Face Images and Attributes to Attributes

Comparative Study of Human Age Estimation Based on Hand-Crafted and Deep Face Features

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