AI-Based Photo Sharing Platform: Apna Photos

Authors

  • Pallavi Scholar B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Javed Warsi Scholar B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Abhishek Sahu Scholar B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Mayank Verma Scholar B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Namita Srivastava Assistant Professor, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT25113342

Keywords:

Facial Recognition, QR Code Authentication, Event Photo Sharing, Secure Media Access, AI-based Image Retrieval, Personalized Photo Management

Abstract

Apna Photos is an AI-enabled photo-sharing platform designed to revolutionize how individuals access and manage their personal photographs from events. Conventional photo distribution methods are often inefficient, insecure, and impersonal requiring users to sift through extensive photo collections or depend on intermediaries. To overcome these limitations, Apna Photos employs facial recognition technology and QR code-based authentication, enabling users to securely and instantly retrieve only their own images from shared event galleries. This platform prioritizes privacy, user experience, and scalability. By leveraging artificial intelligence, it automates the detection and filtering of user-specific images, delivering a tailored and secure experience. Facial biometric verification helps prevent unauthorized access, while QR codes streamline the login process. Extensive real-world testing across different event scenarios showcased the system’s high facial recognition accuracy and significant reduction in retrieval time. User feedback indicated a notable improvement in satisfaction compared to conventional photo-sharing practices. By seamlessly connecting event organizers, photographers, and attendees, Apna Photos offers an efficient, user-friendly solution for sharing memories. Built on robust AI capabilities, it also sets the stage for future innovations such as mobile integration, enhanced personalization, and broader commercial deployment.

Downloads

Download data is not yet available.

References

Abadi, M., Chu, A., Goodfellow, I. J., McMahan, H. B., Mironov, I., & others. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM.

Zhao, Z., & Li, Z. (2019). Blockchain-based secure and transparent access control systems. Journal of Computer Science and Technology, 34(1), 59–76.

Dosovitskiy, A., & Brox, T. (2014). Discriminative unsupervised feature learning with deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1734–1747.

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,1701–1708.

Shobhit Kumar Ravi, Shivam Chaturvedi, Dr. Neeta Rastogi, N. Akhtar, Y. Perwej, “A Framework for Voting Behavior Prediction Using Spatial Data”, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), ISSN: 2347-5552, Volume 10, Issue 2, Pages 19-28, 2022, DOI: 10.55524/ijircst.2022.10.2.4

Wang, C., Zhang, Y., & Yu, J. (2020). User-centric photo management systems: Current challenges and AI-driven solutions. Multimedia Tools and Appl., 79(45), 33609–33631.

Zhao, H., Li, K., & Liu, H. (2021). Artificial Intelligence in Multimedia: Facial recognition applications and data privacy concerns. ACM Computing Surveys, 54(3), 1–35.

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. British Machine Vision Conference (BMVC).

Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 815–823.

Y. Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054

Krombholz, K., Buschek, D., & Holz, T. (2015). QR code security: A survey of attacks and challenges for usability and security. Proceedings of the 11th International Conference on Availability, Reliability and Security, 1–10.

Wang, C., Zhang, Y., & Yu, J. (2020). User-centric photo management systems: Current challenges and AI-driven solutions. Mult. Tools and Applications, 79(45), 33609–33631.

Sachin Bhardwaj, Apoorva Dwivedi, Ashutosh Pandey, Y. Perwej, Pervez Rauf Khan, “Machine Learning-Based Crowd Behavior Analysis and Forecasting”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 418-429, May-June 2023-2023, DOI: 10.32628/CSEIT23903104

Holovaty, A., & Kaplan-Moss, J. (2009). The definitive guide to Django: Web development done right. Apress.

Y. Perwej, S. A. Hann, N. Akhtar, “The State-of-the-Art Handwritten Recognition of Arabic Script Using Simplified Fuzzy ARTMAP and Hidden Markov Models”, International Journal of Computer Science and Telecommunications (IJCST), Sysbase Solution (Ltd), UK, London, ISSN 2047-3338, Volume, Issue 8, Pages, 26 - 32, 2014

Abadi, M., et al. (2016). TensorFlow: A system for large-scale machine learning. USENIX Symposium on Operating Systems Design and Implementation, 265–283.

Zhao, Q., & Li, H. (2019). An improved secure QR code authentication mechanism for mobile applications. Journal of Information Security and Applications, 47, 133–142.

Abadi, M., et al. (2016). TensorFlow: A system for large-scale machine learning. USENIX Symposium on Operating Systems Design and Implementation, 265–283.

Y. Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), SCOPUS, IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 2022, DOI: 10.1109/ICACTA54488.2022.9753501.

Holovaty, A., & Kaplan-Moss, J. (2009). The definitive guide to Django: Web development done right. Apress.

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1701–1708.

Bhavesh Kumar Jaisawal, Y. Perwej, Sanjay Kumar Singh, Susheel Kumar, Jai Pratap Dixit, Niraj Kumar Singh, “An Empirical Investigation of Human Identity Verification Methods” International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 1, Pages 16-38, 2022, DOI: 10.32628/IJSRSET2310012.

Zhao, Q., Zhang, Y., & Li, H. (2019). QR Code Authentication for Secure Media Access. International Journal of Computer Science and Information Security, 17(9), 175–186.

Downloads

Published

26-05-2025

Issue

Section

Research Articles