Improved automatic age estimation algorithm using a hybrid feature selection

2020 ◽  
Vol 12 (1) ◽  
pp. 17
Author(s):  
Santhosh Kumar Gangadharaih ◽  
H.N. Suresh
Keyword(s):  
Feature Selection ◽  
Age Estimation ◽  
Estimation Algorithm

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

Age estimation in Japanese speech based on feature selection

2016 ◽  
Vol 140 (4) ◽  
pp. 3445-3445
Author(s):  
Atsushi Morimoto ◽  
Masahiro Niitsuma ◽  
Yoichi Yamashita
Keyword(s):  
Feature Selection ◽  
Age Estimation

Research on Age Estimation Algorithm Based on Structured Sparsity

2019 ◽  
Vol 33 (06) ◽  
pp. 1956006 ◽  
Author(s):  
Zijiang Zhu ◽  
Junshan Li ◽  
Yi Hu ◽  
Xiaoguang Deng
Keyword(s):  
Age Estimation ◽  
Age Distribution ◽  
Estimation Algorithm ◽  
Absolute Error ◽  
Sparse Learning ◽  
Estimation Model ◽  
Accumulation Index ◽  
Error Accumulation ◽  
Index Curve ◽  
Facial Images

In order to solve the inaccuracy of age estimation dataset and the imbalance of age distribution, this paper proposes an age estimation model based on the structured sparse learning. Firstly, the Multi-label representation of facial images is performed by age, and the age estimation model is trained by solving the model matrix. Finally, the correlation with all age labels is calculated according to the facial images and age estimation model to be tested, and the most correlated age is taken as the predicted age. This paper sets up a series of verification experiments, and analyzes the structured sparse age estimation model from several perspectives. The proposed algorithm has achieved good results in the evaluation of indexes such as the mean absolute error, accumulation index curve and convergence rate, and has designed the demo system to put the model into use. Facts prove that the age estimation model proposed in this paper may achieve a good estimation effect.

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