Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes

Xie, Jiucheng; Pun, Chi‐Man

doi:10.1109/tifs.2019.2902823

Cited by 29 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the decision of every CNN was combined using the voting method which gave the gender classification results accurately [ 47 , 48 , 49 ]. Every patch was evaluated properly and complementary patches were selected for voting purposes [ 50 , 51 , 52 ]. The accuracy of the proposed method was comparatively better than the existing techniques [ 53 ].…”

Section: Literature Workmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

View full text Add to dashboard Cite

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

show abstract

Section: Literature Workmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Since there have been no works that conducted experiments on SE protocol of this database, we are not able to compare our SE results with those of other works. With the same RS protocol as [45] and [46], our method outperforms the best reported result [46] by as much as 0.82 years. Experimental results are tabulated in Table 4.…”

Section: ) Real Age Estimationmentioning

confidence: 78%

Soft-Ranking Label Encoding for Robust Facial Age Estimation

2020

View full text Add to dashboard Cite

Automatic facial age estimation can be used in a wide range of real-world applications. However, this process is challenging due to the randomness and slowness of the aging process. Accordingly, in this paper, we propose a novel method aimed at overcoming the challenges associated with facial age estimation. First, we propose a novel age encoding method, referred to as 'Soft-ranking', which encodes two important properties of facial age, i.e., the ordinal property and the correlation between adjacent ages. Therefore, Soft-ranking provides a richer supervision signal for training deep models. Moreover, we carefully analyze existing evaluation protocols for age estimation, finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods. Moreover, we achieve state-of-the-art performance on four most popular age databases, i.e., Morph II, AgeDB, CLAP2015, and CLAP2016.

show abstract

“…Directly using CNN on different categories of images may not give a sufficient output. It is found that diverse combinations of attributes which are in connection with age can aid in the most effective age predictor [31].…”

Section: Cnn Methods Applied In Age Predictionmentioning

confidence: 99%

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

Kathi¹,

Shaik²

2021

View full text Add to dashboard Cite

One of the key challenges that the computer vision is facing is the age prediction. A well efficient CNN is selected for age prediction by performing various CNN operations by taking the categories as age 40 and above age 40. The selected CNN method obtained a training accuracy of 100% at more than 100 epochs. Hence, 100 epochs is considered for training. At this, the validation accuracy achieved is 84.9%. Three kinds of age phases with an age gap of 20,10 and 5 are used to predict the age. The normal method results in very less accuracy. Hence a hierarchical method is formulated. Under the hierarchical method, CNN is trained to estimate the age gaps in decreasing order. Hence not a single classifier, a group of classifiers are used for testing the image. From traditional method to hierarchical method, the 20 age gap accuracy increased from 27% to above 60%, ten age gap increased from 12% to above 35%, and five age gap increased from 5.5% to above 21%. To improve further, the features of the face parts are derived and combined which improves the efficiency compared to normal method, but not good accuracy as Hierarchical method. The combination of hierarchical method along with the face feature extraction method results in a considerable improvement in accuracy.

show abstract

Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes

Cited by 29 publications

References 45 publications

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Soft-Ranking Label Encoding for Robust Facial Age Estimation

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

Contact Info

Product

Resources

About