The Application of Artificial Intelligence Facial Recognition Technology in Tourism Visual Marketing

Hongxia Su; Shan Sun

doi:10.62051/ijgem.v7n3.26

Authors

Hongxia Su
Shan Sun

DOI:

https://doi.org/10.62051/ijgem.v7n3.26

Keywords:

Artificial intelligence, Travel visual marketing, Facial recognition technology, Application

Abstract

This article focuses on the application of artificial intelligence in tourism visual marketing, emphasizing the practical forms, opportunities, and challenges of facial recognition and facial emotion recognition technology. Facial recognition achieves precise identity and interest matching, while facial emotion recognition analyzes real-time expressions to determine emotional states. Together, they can deliver personalized services, enhance interactive experiences, optimize advertising placements, and product development, bringing opportunities such as improved customer experience, increased operational efficiency, enhanced market competitiveness, and innovative products and services to the tourism industry. [14] However, the application of these technologies also faces challenges such as data privacy leaks, insufficient recognition accuracy, and social ethical controversies. This study aims to provide practical guidelines for tourism companies to formulate AI visual marketing strategies, helping to balance technological application with visitor rights and achieve sustainable development.

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References

[1] Cong hui Li (2025). Research on the Application of Facial Recognition Technology in Intelligent Psychological Analysis. Shanxi electronic technology. 03: 95-97+110.

[2] ZhaoW, ChellappaR (2003), Face cognition: Al iterature survey, ACMC puting Surveys. 35(4).

[3] LinCao, Du Kangning, Guo Yanan (2021). Face recognition algorithms and case analysis. Beijing: Electronics Industry Press. 7, 1-10

[4] Yinghui Wang (2010). Face recognition: principles, methods, and technologies. Beijing: Science Press. 134(06), 96-99.

[5] Sharma, M., Anuradha, J., Manne, K.H., & Kashyap, C.S.G. (2017). Using deep learning for facial detection. IOP Conference Series: Materials Science and Engineering. 263(4), 42-92.

[6] Xingyu W (2024). Research on Facial Emotion Recognition Technology Based on Convolutional Neural Networks. Dalian Jiao Tong University. 134(06), 78-95.

[7] Mondol. E.P, Salman. N.A, Rahid.A. O, etal (2021). The impact of visual marketing on consumers' purchasing intentions in fashion retail stores. International Academic Journal of Business and Social Sciences.11, 7-18.

[8] Purahong, B., Chutchavong, V., Aoyama, H., and Pintavirooj, C. (2020). Mixed facial features and their applications in face recognition. Mobile multimedia magazine. 16(1–2), 245–266.

[9] Kortli, Yassin, Maher Jridi, Ayman AlFalou, and Mohamed Atri (2020). Face Recognition Systems: ASurvey. 20, 2-342.

[10] N. Srinivas, K. Ricanek, D. Michalski, D.S. Bolme, and M. King, Facial (2019). Algorithm bias: Performance differences on images of children and adults. Computer Vision and Pattern Recognition Workshop.6, 2269–2277.

[11] R. Vera-Rodríguez, M. Blázquez, A. Morales, E. Gonzalez-Sosa, J.C. Neves, and H. Proença (2019). Utilizing gender information in facial recognition systems using deep convolutional neural networks. Computer Vision and Pattern Recognition Workshop.6, 2254–2260.

[12] J.J. Howard, Y.B. Sirotin, and A.R. Vemury (2019). The impact of broad and specific population homogeneity on the distribution of disguise rate and false match rate in facial recognition algorithm performance. Biometric theory and application systems. 9:1–8.

[13] Lei Zhu. (2021). Reflection striggered by a facial recognition dispute incident. Traffic management.11, 24-60.

[14] Liu, S.F., Liu, H.H., Chang, J.H., and Chou, H.N. (2019). Analysis of a new visual marketing craze: the effect of LINE sticker features and user characteristics on download willingness and product purchase intention. Asia Pacific Manage.24, 263–277.

[15] Huang, J., Wang, J., Liu, H., and Wang, Z. (2020). Visual marketing and consumer behavior. Sci. Decision Making. 4: 67–89.

[16] Pauluzzo, R., and Mason, M.C. (2022). Amulti-dimensional view of consumer value to explain socially-responsible consumer behavior: a fuzzy-setan alysis of Generation Y’s fast-fashion consumers. J.Market. Theor. Prac. 30, 191–212.

[17] Sample, K.L., Hagtvedt, H., and Brasel, S.A. (2020). Components of visual perception in marketing contexts: a conceptual frame work and review. J.Acad. Market. Sci. 48, 405–421.

[18] Zhang ting (2025). Current Status and Optimization Strategies for Book Short Video Marketing in the New Media Era. The world of public relations. 12, 28-30.

[19] Lihuiyin (2025). Research on the development and application of facial recognition technology in video images. Electronic Design Engineering. 29(05), 164-168.

[20] Wang Fuping, Wang Dingsha, Li Ou, Liu Weihua, and Liu Hongwei (2025). An occlusion face recognition algorithm based on fine-grained deep feature masking estimation. Journal of Xi'an Jiao Tong University. 59(02), 170-179.

[21] Zhou Weiliang (2025). Analysis of tourist emotions on the G217 National Highway using facial expression recognition technology. Journal of Yunnan Minzu University (Natural Sciences Edition). 22:1-13.

[22] Zhang Jie Mei & Zhang Zi Wei. (2023). How does visual marketing stimulate the purchasing intention of viewers in travel e-commerce live broadcasts?. Economic and Management Review, 39(05), 138-149.

[23] Chen Jiyang and Zou Qianqian. (2021). A Comparative Study on the Impact of Sensory Marketing on Consumer Loyalty in Agricultural Tourism: An Empirical Analysis Based on the Experience of Fujian Tea Tourism Marketing. Fujian tea, 43, (10), 89-90.

[24] LIL, MUX, LIS, (2020). A Review of Face Recognition Technology [J/OL]. IEEE Access, 8:139110-139120.

[25] V.S. Manjula and L.D.S.S. Baboo (2012). Using an image database, facial recognition, identification, and tracking are performed through the PRDIT algorithm for criminal investigations. Int.J. Comput.38, 40-46.

[26] K. Lander, V. Bruce, and M. Bindemann (2018), Utilizing individual-difference-based facial recognition fundamental research: Impacts on criminal investigations and security. Cognit. Res., Princ. Implications, vol.3, no.1, pp.113, Dec.2018.

[27] Y. Hu, H. An, Y. Guo, C. Zhang, T. Zhang, and L. Ye (2010), The development status and prospects on the face recognition, inProc.4th Int. Conf. Bioinf. Biomed. Eng., Jun.2010, pp.14.

[28] Zhang, J., and Zhang, S. (2023). How does visual marketing evoke the purchase intention of live broadcast viewers in travel e-commerce? Econ. Manage. 39, 138–149.

[29] Yu, H., Xie, Y., and Chen, X. (2018). Visual marketing eye movement research: review and outlook. Foreign Econ. Manage. 40, 98–124.