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@article{170724,
        author = {Vijendra Lad and Manasa Gopavaram and Nandini Yera and Swati Koulagi and Ambeshree Koli and Shrushti Shete},
        title = {Age and Gender Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {529-532},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170724},
        abstract = {The project Title is “Age and Gender Prediction” This paper presents a deep learning-based approach for age and gender prediction from facial images using YOLOv5 and OpenCV. The YOLOv5 (You Only Look Once) model, known for its efficiency in real-time object detection, is utilized to extract facial features from input images. These features are then processed by a custom neural network to predict the age and gender of individuals. OpenCV is used for image pre-processing and handling various computer vision tasks such as face detection and alignment. The proposed system achieves accurate predictions while maintaining high processing speed, making it suitable for real-time applications in fields like security, human-computer interaction, and personalized services. The paper also discusses the challenges in dataset variability, model training, and generalization across diverse populations.},
        keywords = {Age prediction, Gender prediction, Deep learning, YOLOv5, OpenCV, Facial recognition, Object detection, Convolutional Neural Networks (CNNs), Real-time prediction, Face detection, interaction, Computer vision.},
        month = {December},
        }