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. 2022 Aug 24:2022:1413597.
doi: 10.1155/2022/1413597. eCollection 2022.

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

S Haseena  1 , S Saroja  1 , R Madavan  2 , Alagar Karthick  3 , Bhaskar Pant  4 , Melkamu Kifetew  5
Affiliations

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

S Haseena et al. Comput Math Methods Med. .

Abstract

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D-dimensional characteristics from the source face image, followed by the feature selection strategy. The face's distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person's age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Overview of proposed nutrition recommendation system.
Figure 2
Figure 2
Proposed nutrition recommendation system.
Figure 3
Figure 3
Schematic diagram of the proposed network.
Figure 4
Figure 4
Classification of age and gender.
Figure 5
Figure 5
Image preprocessing: (a) input image; (b) face landmark; (c) face detection.
Figure 6
Figure 6
Features extracted from DCNN.
Figure 7
Figure 7
Classification results: (a) predicted age; (b) predicted gender; (c) combined output.
Figure 8
Figure 8
Classification results of males across all ages.
Figure 9
Figure 9
Classification results of females across all ages.
Figure 10
Figure 10
Nutrition recommendation based on predicted age and gender.
Figure 11
Figure 11
Visualization of 68 facial landmark coordinates in the dataset.
Figure 12
Figure 12
Performance of image preprocessing module for (a) age and (b) gender classifications.
Figure 13
Figure 13
Experimental results of feature extraction techniques.
Figure 14
Figure 14
Experimental results of feature selection techniques.
Figure 15
Figure 15
Misclassifications results of (a) age and (b) gender.
Algorithm 1
Algorithm 1
Algorithm for particle swarm optimization.
Algorithm 2
Algorithm 2
Algorithm of hybrid PSO.
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References

    1. Wang X., Liang L., Wang Z., Hu S. Age estimation by facial images: a survey. China Journal of Image and Graphics . 2012;17(6):603–622.
    1. Cottrell G. W., Metcalfe J. Face emotion and gender recognition using holons. Proceedings of Conference on Advances in Neural Information Processing Systems . 1990;3:564–571.
    1. Zhao W., Chellappa R., Phillips P. J., Rosenfeld A. Face recognition. ACM Computing Surveys . 2003;35(4):399–458. doi: 10.1145/954339.954342. - DOI
    1. Baluja S., Rowley H. Boosting sex identification performance. International Journal of Computer Vision . 2007;71(1):111–119. doi: 10.1007/s11263-006-8910-9. - DOI
    1. Moghaddam B., Yang M. Gender classification with support vector machines. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition; 2000; Grenoble, France. pp. 306–311.

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