scholarly journals Age Label Distribution Learning Based on Unsupervised Comparisons of Faces

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qiyuan Li ◽  
Zongyong Deng ◽  
Weichang Xu ◽  
Zhendong Li ◽  
Hao Liu
Keyword(s):  
Age Estimation ◽  
State Of The Art ◽  
Processing Method ◽  
Wireless Sensor ◽  
Learning Method ◽  
Significant Progress ◽  
Meaningful Information ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Facial Age

Although label distribution learning has made significant progress in the field of face age estimation, unsupervised learning has not been widely adopted and is still an important and challenging task. In this work, we propose an unsupervised contrastive label distribution learning method (UCLD) for facial age estimation. This method is helpful to extract semantic and meaningful information of raw faces with preserving high-order correlation between adjacent ages. Similar to the processing method of wireless sensor network, we designed the ConAge network with the contrast learning method. As a result, our model maximizes the similarity of positive samples by data enhancement and simultaneously pushes the clusters of negative samples apart. Compared to state-of-the-art methods, we achieve compelling results on the widely used benchmark, i.e., MORPH.

scholarly journals Label Enhancement for Label Distribution Learning via Prior Knowledge

2020 ◽  
Author(s):  
Yongbiao Gao ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Emotion Recognition ◽  
Prior Knowledge ◽  
Decision Process ◽  
Age Estimation ◽  
State Of The Art ◽  
Learning Agent ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to an instance. However, most of training datasets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. We propose to use the prior knowledge to recover the label distributions. The process of recovering the label distributions from the logical labels is called label enhancement. In this paper, we formulate the label enhancement as a dynamic decision process. Thus, the label distribution is adjusted by a series of actions conducted by a reinforcement learning agent according to sequential state representations. The target state is defined by the prior knowledge. Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition.

Facial Age Estimation by Learning Label Distribution CNN

2021 ◽  
Author(s):  
Kuan-Hsien Liu ◽  
Chun-Te Chang ◽  
Tsung-Jung Liu
Keyword(s):  
Age Estimation ◽  
Label Distribution ◽  
Facial Age

scholarly journals Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhao ◽  
Han Wang ◽  
Guang-Bin Huang
Keyword(s):  
Age Estimation ◽  
Optimization Problems ◽  
State Of The Art ◽  
Mathematical Optimization ◽  
The State ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Training Process ◽  
Ordinal Ranking ◽  
Facial Age

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster) while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

Co-LDL: A Co-training-based Label Distribution Learning Method for Tackling Label Noise

2021 ◽  
pp. 1-1
Author(s):  
Zeren Sun ◽  
Huafeng Liu ◽  
Qiong Wang ◽  
Tianfei Zhou ◽  
Qi Wu ◽  
...  
Keyword(s):  
Learning Method ◽  
Label Noise ◽  
Label Distribution Learning ◽  
Label Distribution

scholarly journals Label Distribution Learning by Regularized Sample Self-Representation

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Wenyuan Yang ◽  
Chan Li ◽  
Hong Zhao
Keyword(s):  
Least Squares ◽  
Linear Relationship ◽  
Linear Combination ◽  
State Of The Art ◽  
Distribution Problem ◽  
Learning Framework ◽  
Multilabel Learning ◽  
Label Distribution Learning ◽  
Public Datasets ◽  
Label Distribution

Multilabel learning that focuses on an instance of the corresponding related or unrelated label can solve many ambiguity problems. Label distribution learning (LDL) reflects the importance of the related label to an instance and offers a more general learning framework than multilabel learning. However, the current LDL algorithms ignore the linear relationship between the distribution of labels and the feature. In this paper, we propose a regularized sample self-representation (RSSR) approach for LDL. First, the label distribution problem is formalized by sample self-representation, whereby each label distribution can be represented as a linear combination of its relevant features. Second, the LDL problem is solved by L2-norm least-squares and L2,1-norm least-squares methods to reduce the effects of outliers and overfitting. The corresponding algorithms are named RSSR-LDL2 and RSSR-LDL21. Third, the proposed algorithms are compared with four state-of-the-art LDL algorithms using 12 public datasets and five evaluation metrics. The results demonstrate that the proposed algorithms can effectively identify the predictive label distribution and exhibit good performance in terms of distance and similarity evaluations.

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