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@article{162144,
        author = {N C SHREEDEVI and JHANAVI R and KEERTHI RAJU L and NITESH M A and SHREENIDHI B S},
        title = {GENDER AND AGE PREDICTION USING FACE RECOGNITION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {8},
        pages = {70-74},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162144},
        abstract = {This research project introduces an advanced Python-based system for real-time gender and age detection using OpenCV with the MTCNN model. Addressing the growing demand for accurate facial attribute recognition, the system employs a multi-task cascaded convolutional network to precisely detect and extract faces in real-time video streams. Through the integration of pre-trained deep learning models, the project achieves gender and age classification, providing valuable insights into the demographics of individuals captured in the video feed. The implementation incorporates threading to optimize processing speed, ensuring efficient and seamless performance. With its applications extending to security, retail analytics, and human-computer interaction, this project contributes to the evolving landscape of computer vision technologies.},
        keywords = {Python, OpenCV, MTCNN.},
        month = {},
        }