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@article{197372,
        author = {Dr. Ashok Kumar Yadav and Ritik Rana and Mohit Mehra and Sakshi Rao},
        title = {ADVANCE GENDER AND AGE DETECTION USING CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6789-6798},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197372},
        abstract = {Currently exploration has explored to rooting supplementary information from colorful biometric ways similar as fingerprints, face, iris, win, voice etc. This information contains some features like gender, age, beard, moustache, scars, height, hair, skin color, spectacles, weight, facial marks, tattoos etc. All this information contributes further and further during identification. The major changes that come across face recognition is to find age & gender of the person. This paper contributes a significant check of colorful ace recognition ways for chancing the age and gender. The being ways are bandied grounded on their performances. This paper also provides unborn directions for farther exploration. Age and gender, two of the crucial facial attributes, play a veritably foundational part in social relations, making age and gender estimation from a single face image an important task in intelligent operations similar as access control, mortal- computer commerce, law enforcement marketing. This design is grounded upon computer vision the colorful languages used to reuse image and descry age and gender of the person from the image. Convolutional Neural Network is a deep neural network, DNN which is applied for the image recognition and processing tasks and NLP tasks. It is also referred to as CNN and is composed of input/output layers as well as many hidden layers, where most of them are convolutional. In some sense, ConvNets could be seen as regularized Multilayer Perceptrons.
In this paper, we demonstrate that learning representations using the highly effective Convolutional Neural Network models results in a large improvement in performance in the described tasks. We introduce a straightforward approach towards Convolutional Neural Network model engineering that can be applied even in case the amount of data is restricted. As an example of our approach applicability, we conduct experiments on the new Audience dataset for face attribute prediction task and prove the superior performance compared to the state-of-the-art methods.},
        keywords = {Age and Gender Detection, Machine Learning, Deep Learning, Convolutional Neural Networks, Face Recognition, Feature Extraction, Age Estimation, Gender Classification, Computer Vision, Soft Biometrics.},
        month = {April},
        }