Development of a Smart Digital Advertisement Board Based on Face Recognition System

Fahri Heltha, Sharandhass Radakrishnan, Haris Wahyudi, Aulia Rahman


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Abstract. We develop a smart digital advertisement board system which allows the system to display advertisements based on the majority of age and gender classifications of the consumers. The system captures the faces of the crowd and the face recognition techniques used to classify the majority gender and age of the crowd and then shows appropriate advertisement from the database to the advertisement board. A DNN model that is built, trained, and validated is used to recognize and predict the age and gender of the visible faces through image input or webcam using face photo dataset known as audience dataset. Several testing and analysis have been done onto the system in order to demonstrate the effectiveness and reliability of the system in displaying suitable advertisement for the public. The system can get gender accuracy of 77.82% and 86% for female and male respectively. And 68.78% accuracy for age recognition. The recognition speed is less than 1.3 second for up to 9 faces in an input image.


face recognition; age and gender recognition; smart advertisement; DNN; OpenCV

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Bove?e, C., & Arens, W. (1992). Contemporary advertising (p. 7). Homewood, Ill.: Irwin

The Economic Impact of Advertising Expenditures in The United States. (2013). Retrieved 8 February 2021, from

Fuxman, L., Elifoglu, I., Chao, C., & Li, T. (2018). Digital Advertising: A More Effective Way to Promote Businesses Products. Journal of Business Administration Research, 3(2), 59. doi: 10.5430/jbar.v3n2p59

Suruhanjaya Komunikasi dan Multimedia Malaysia. (2009). Advertising Development in Malaysia: Catching Eyeballs in Changing Media (p. 4). Selangor

Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, Present, and Future of Face Recognition: A Review. Electronics (Switzerland), 9(8), 153.

Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 3458.

Thorat, S. B., Wolf, H. C., & Bisson, C. (2010). Facial Recognition Technology : An Analysis with Scope in India. (IJCSIS) International Journal of Computer Science and Information Security, 8(1), 325330.

Lin, S. H. (2000). An Introduction to Face Recognition Technology. Informing Science Special Issue on Multimedia Informing Technologies- Part 2, 3(1), 17.

Yang, G., & Huang, T. S. (1994). Human Face Detection in a Complex Background. Pattern Recognition, 27(1), 5363.

Kotropoulos, C., & Pitas, I. (1997). Rule-based Face Detection in Frontal Views. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4, 25372540.

Yow, K. C., & Cipolla, R. (1997). Feature-based human face detection. Image and Vision Computing, 15(9), 713735.

Viola, P., & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1(July 2014).

Guo, G., Li, S. Z., & Chan, K. (2000). Face Recognition by Support Vector Machines. Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000, 196201.

Ahonen, T., Hadid, A., & Pietikinen, M. (2004). Face recognition with local binary patterns. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3021, 469481. 24670-1_36

Bolme, D. S., Beveridge, J. R., Teixeira, M., & Draper, B. A. (2003). The CSU face identification evaluation system: Its purpose, features, and structure. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2626(May 2014), 304313. 540-36592-3_29

Sawant, M. (2019). Gender-and-Age-Detection. Retrieved June 16, 2021, from

Hassner, T., & Levi, G. (2014). Face Image Project - Data. Retrieved June 16, 2021, from


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