Learning universal multi-view age estimator using video context

Zheng, Shuang; Ni, Bingbing; Guo, Dong; Sim, Terence; Yan, Shuicheng

doi:10.1109/iccv.2011.6126248

Cited by 51 publications

(29 citation statements)

References 15 publications

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“…Yan et al [25] divided one global face image into several patches, then DCT transform is applied to extract feature from all the patches. Bio-Inspired feature (BIF) is applied as the aging representation in [9,10,21].…”

Section: A Aging Feature Representationmentioning

confidence: 99%

Can We Minimize the Influence Due to Gender and Race in Age Estimation?

Wang

et al. 2013

2013 12th International Conference on Machine Learning and Applications

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Automatic human age estimation has attracted a great deal of interest in the past few years. Although many advancements have been made by researchers, there are still many challenges: such as age estimation across different image acquisition methods, different expressions, gender and races. The influence due to race and gender seems to be the most common issue, because collecting a large amount of face images with comprehensive racial diversities seems impractical. The performance will degrade when estimating face images of races that differ from the training set. In this work, we present a new scheme to mitigate the influences of race and gender in the problem of age estimation. Our system will contribute a robust solution to solve the problem of age estimation across races and genders. This study is essential for developing a practical age estimation system (with mixture of races and gender.) To evaluate the performance of the proposed algorithm, we run comprehensive experiments on one widely used big database -MORPH-II, which contains more than 55, 000 images. On an average, an improvement of more than 20% has been achieved using the proposed scheme.

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Section: A Aging Feature Representationmentioning

confidence: 99%

Can We Minimize the Influence Due to Gender and Race in Age Estimation?

Wang

et al. 2013

2013 12th International Conference on Machine Learning and Applications

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“…Human age estimation is an active research topic in computer vision and pattern recognition in recent years [15] [3] [27] [26] [31] [2] [23], because of many potential applications [6] [21], e.g., age-specific human-computer interaction [9], and business intelligence [24]. However, age estimation is very challenging, especially on a large database with heterogeneous populations [11].…”

Section: Introductionmentioning

confidence: 99%

A Study on Cross-Population Age Estimation

Guo

Zhang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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We study the problem of cross-population age estimation. Human aging is determined by the genes and influenced by many factors. Different populations, e.g., males and females, Caucasian and Asian, may age differently. Previous research has discovered the aging difference among different populations, and reported large errors in age estimation when crossing gender and/or ethnicity. In this paper we propose novel methods for cross-population age estimation with a good performance. The proposed methods are based on projecting the different aging patterns into a common space where the aging patterns can be correlated even though they come from different populations. The projections are also discriminative between age classes due to the integration of the classical discriminant analysis technique. Further, we study the amount of data needed in the target population to learn a cross-population age estimator. Finally, we study the feasibility of multi-source crosspopulation age estimation. Experiments are conducted on a large database of more than 21,000 face images selected from the MORPH. Our studies are valuable to significantly reduce the burden of training data collection for age estimation on a new population, utilizing existing aging patterns even from different populations.

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“…Age estimation, i.e., predicting the age from a face image, has long been an active research topic in computer vision, with many applications such as age-based face retrieval [1], precision advertising [2], intelligent surveillance [3], human-computer interaction (HCI) [4] and internet access control [2].…”

Section: Introductionmentioning

confidence: 99%

D2C: Deep cumulatively and comparatively learning for human age estimation

Xing

et al. 2017

Pattern Recognition

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Age estimation from face images is an important yet difficult task in computer vision. Its main difficulty lies in how to design aging features that remain discriminative in spite of large facial appearance variations. Meanwhile, due to the difficulty of collecting and labeling datasets that contain sufficient samples for all possible ages, the age distributions of most benchmark datasets are often imbalanced, which makes this problem more challenge. In this work, we try to solve these difficulties by means of the mainstream deep learning techniques.Specifically, we use a convolutional neural network which can learn discriminative aging features from raw face images without any handcrafting. To combat the sample imbalance problem, we propose a novel cumulative hidden layer which is supervised by a point-wise cumulative signal. With this cumulative hidden layer, our model is learnt indirectly using faces with neighboring ages and thus alleviate the sample imbalance problem. In order to learn more effective aging features, we further propose a comparative ranking layer which is supervised by a pair-wise comparative signal. This comparative ranking layer facilitates aging feature learning and improves the performance of the main age estimation task. In addition, since one face can be included in many different

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Learning universal multi-view age estimator using video context

Cited by 51 publications

References 15 publications

Can We Minimize the Influence Due to Gender and Race in Age Estimation?

Can We Minimize the Influence Due to Gender and Race in Age Estimation?

A Study on Cross-Population Age Estimation

D2C: Deep cumulatively and comparatively learning for human age estimation

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