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@article{145739,
        author = {SHARA M S and SHEMITHA P A},
        title = {Facial Age Estimation Based On Multiple CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {593-599},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145739},
        abstract = {Facial age estimation becomes more challenging because of its various applications such as security control, electronic vending machines, forensic art, entertainment, and cosmetology. However, regardless of the ongoing growth in the field of age estimation, it is still a challenging job as the process of facial aging is affected not only by intrinsic factors like change in shape and size of face, but also by extrinsic factors, such as manner of living, eating habits and environment. Furthermore, surgical marks, the presence of facial scars, facial cosmetics and even dense facial hair can create hindrance in accurate working of facial age estimation systems. Estimating age from images has been historically one of the most challenging problems within the field of facial analysis. With the rapid advances in computer vision and pattern recognition, computer-based age estimation on faces becomes a particularly interesting topic. So for the facial age estimation, propose multiple deep Convolutional Neural Networks and also contribute a data set, including more than 10000 face images attached with their labeled age.},
        keywords = {Face, facial age estimation, face detection, feature extraction. },
        month = {},
        }