Deep Learning Based Human Age, Gender, and BMI Recognition
DOI:
https://doi.org/10.46610/IJNSA.2025.v010i01.001Keywords:
Age detection, BMI detection, Classification, Deep convolutional neural network, Gender detection, Machine learningAbstract
Age, gender, and body mass index (BMI) recognition play a crucial role in network security, personal healthcare, and content personalization. Applications include age-based content regulation, enhancing user experiences on social media through targeted advertisements, and optimizing medical strategies. Leveraging facial recognition technology, this study introduces a deep convolutional neural network (CNN)-based method for accurately predicting age, gender, and BMI. The proposed approach integrates both real-time image acquisition and pre-loaded images to facilitate real-time predictions. Utilizing a machine-learning-driven technique, an object detection and classification cascade function is employed on subjects using a dataset of labeled positive and negative images. By implementing these methods and a streamlined CNN, this study contributes to advancements in facial recognition and related fields.
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