Human Age Estimation Based on Multi-level Local Binary Pattern and Regression Method

Nguyen, Dat Tien; Cho, So Ra; Park, Kang Ryoung

doi:10.1007/978-3-642-55038-6_67

Cited by 20 publications

(44 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the two estimation results, we can see that, although some errors occurred during the blurred image classification step, the performance of the age estimation system is reduced slightly (6.91 years versus 6.845 years). Compared with the results of the previous methods listed in Table 6, we can conclude that our method is more efficient at making age estimation systems robust to blurring effects, compared with previous methods [14,16]. Table 10.…”

Section: Performance Evaluation Of Our Methodsmentioning

confidence: 71%

“…In the first experiment, we evaluated the performance of previous age estimation systems [13,14,16]. We used the PAL databases described in Table 3 for this experiment.…”

Section: Performance Evaluation Of Previous Age Estimation Systemsmentioning

confidence: 99%

“…[14][15][16]. The advantage of the LBP method is that it can extract both local and global skin features by dividing the facial region into local sub-blocks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Age Estimation-Based Soft Biometrics Considering Optical Blurring Based on Symmetrical Sub-Blocks for MLBP

2015

View full text Add to dashboard Cite

Because of its many useful applications, human age estimation has been considered in many previous studies as a soft biometrics. However, most existing methods of age estimation require a clear and focused facial image as input in order to obtain a trustworthy estimation result; otherwise, the methods might produce increased estimation error when an image of poor quality is used as input. Image blurring is one of major factors that affect estimation accuracies because it can cause a face to appear younger (i.e., reduce the age feature in the face region). Therefore, we propose a new human age estimation method that is robust even with an image that has the optical blurring effect by using symmetrical focus mask and sub-blocks for multi-level local binary pattern (MLBP). Experiment results show that the proposed method can enhance age estimation accuracy compared with the conventional system, which does not consider the effects of blurring.

show abstract

Section: Performance Evaluation Of Our Methodsmentioning

confidence: 71%

“…In the first experiment, we evaluated the performance of previous age estimation systems [13,14,16]. We used the PAL databases described in Table 3 for this experiment.…”

Section: Performance Evaluation Of Previous Age Estimation Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Age Estimation-Based Soft Biometrics Considering Optical Blurring Based on Symmetrical Sub-Blocks for MLBP

2015

View full text Add to dashboard Cite

show abstract

“…Previous work [18] proved that age estimation systems could be affected by misalignment of the face region. Thus, we use the detected positions of the eyes to compensate for the in-plane rotation of the face [13]. In detail, within the detected face region, the positions of right and left eyes are located as ( R x , R y ) and ( L x , L y ), respectively.…”

Section: Age Estimation System: Overviewmentioning

confidence: 99%

Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression

Nguyen

Cho

Shin

et al. 2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation.

show abstract

“…The current state-of-the-art method for local-based age estimation is proposed by Juha et al [18] and Nguyen et al [19]. The method is based on the local binary patterns (LBP) and generates feature representation through face patches.…”

Section: Related Workmentioning

confidence: 99%

Will Wrinkle Estimate the Face Age?

Yap

Costen

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

View full text Add to dashboard Cite

Human Age Estimation Based on Multi-level Local Binary Pattern and Regression Method

Cited by 20 publications

References 9 publications

Age Estimation-Based Soft Biometrics Considering Optical Blurring Based on Symmetrical Sub-Blocks for MLBP

Age Estimation-Based Soft Biometrics Considering Optical Blurring Based on Symmetrical Sub-Blocks for MLBP

Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression

Will Wrinkle Estimate the Face Age?

Contact Info

Product

Resources

About