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AI-Based Photo Sharing Platform: Apna Photos

Author

Listed:
  • Pallavi
  • Javed Warsi
  • Abhishek Sahu
  • Mayank Verma
  • Namita Srivastava

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.

Suggested Citation

  • Pallavi & Javed Warsi & Abhishek Sahu & Mayank Verma & Namita Srivastava, 2025. "AI-Based Photo Sharing Platform: Apna Photos," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(3), pages 790-797, June.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i3:id:1522
    DOI: 10.32628/CSEIT25113342
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113342
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