Published on: June 2026
CROWD BASED ADVERTISING
Abdul Rahman J
M.VASUKI
Article Status
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Abstract
This project presents an automated Face Detection–Based Advertisement Display System that dynamically plays relevant advertisements according to a viewer’s age and gender. Using OpenCV’s deep learning module, the system employs pre-trained Caffe models to detect faces and classify each detected face into defined age groups and gender categories. After classification, an internal advertisement database maps each demographic group to a set of customized ads, including both image and video formats. The application captures frames through a webcam, extracts faces using a DNN-based face detector, and predicts gender and age for each detected face. Based on the prediction, a corresponding list of advertisements is selected and displayed sequentially. The system supports multiple display formats such as fullscreen, banner, and video mode, with an option for automatic mode selection based on age group. The implementation ensures real-time processing and smooth advertisement playback, making it suitable for digital kiosks, shopping malls, retail stores, and smart advertising environments where personalized content delivery enhances customer engagement.
How to Cite this Paper
J, A. R. (2026). Crowd Based Advertising. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.177
J, Abdul. "Crowd Based Advertising." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.177.
J, Abdul. "Crowd Based Advertising." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.177.
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References
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Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Jun 13 2026
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