The Wrinkle Detection System using Facial Features is an innovative project developed with Python, TensorFlow, and OpenCV that focuses on the automated detection of wrinkles on human faces. This system goes beyond conventional wrinkle detection by considering the age of the individual. If the detected age is greater than 45 years, the system classifies and reports the presence of wrinkles; otherwise, it indicates their absence.
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Age-Adaptive Wrinkle Detection: Accurately detects and classifies wrinkles on human faces based on age. Wrinkle detection is performed specifically for individuals aged 45 years or older.
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Facial Feature Analysis: Utilizes advanced computer vision techniques to analyze facial features, identifying fine lines and wrinkles with precision.
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Customizable and Extensible: This open-source project can be customized and extended to suit various applications, such as skincare analysis or age-related research.
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State-of-the-Art Models: Employs deep learning models and facial feature extraction techniques to achieve accurate wrinkle detection.
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Dataset: Utilizes the UTKFace dataset from Kaggle for training and testing the wrinkle detection model.
The system leverages deep neural networks to extract facial features and predict the age of the individual. If the predicted age is above the threshold (e.g., 45), the system proceeds to wrinkle detection using OpenCV and TensorFlow. The detected wrinkles are then reported, providing valuable insights for skincare analysis and age-related research.
We would like to acknowledge the contributions of the open-source community and the developers behind TensorFlow and OpenCV for making this project possible.