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@article{155673,
        author = {Amey S. Pankanti and Harsha V. Talele and Payal K. Bhat and Jayashri R. Patil and Renuka S. Patil},
        title = {Age And Gender Detection Using OpenCV  (OpenSource Computer Vision Library) },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {1428-1431},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155673},
        abstract = {In this paper we have a tendency to propose a deep learning answer to age estimation and gender recognition. By exploitation deep learning concepts, we'll simply classify age and gender with additional accuracy although we've less refined values. We are using Kaggle dataset which is that the greatest available dataset of human faces for training. It contains all the Meta information. For age and gender classification we are using Keras high level API of TensorFlow. Keras is employed for building and training our model. Whether we've less data we can easily interpret gender and age by using TensorFlow the core open source library to help develop our model. To the top we demonstrate that our proposed method will show better results on age and gender estimation as compared to other methods.},
        keywords = {CNN, TensorFlow, OpenCV and Kaggle.},
        month = {},
        }