During the development process, we encountered several challenges that required creative problem-solving and adaptation. One significant hurdle was integrating multiple pre-trained models for face detection, gender prediction, and age estimation. This task posed compatibility issues and formatting challenges, which we addressed by thoroughly studying model documentation and experimenting with different configurations until achieving successful integration. Another challenge was ensuring real-time processing for live video streams with multiple faces, especially on devices with limited resources. To overcome this, we optimized performance through techniques such as model quantization and streamlining inference pipelines. Inconsistent lighting and facial poses also posed challenges, affecting the accuracy of age and gender predictions. We tackled this by augmenting training data to simulate diverse conditions, ensuring model robustness against real-world variability. Additionally, mitigating biases in pre-trained models and designing an intuitive user interface were significant challenges that required careful consideration and iterative refinement based on user feedback. Despite these obstacles, our collaborative efforts and problem-solving skills enabled us to deliver a robust solution for efficient age and gender analysis from human faces.