Face Age Detection
Face age detection, also known as age estimation, refers to the automatic prediction or estimation of a person's age by analyzing facial features in images or videos. Its goal is to assign an age-related value or age range to a given facial image or video frame.
Implementation Methods
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Traditional Feature-based Methods: Early face age detection often used traditional handcrafted features, such as Local Binary Patterns (LBP) and its variants. These methods extract texture features from facial images to characterize age-related information. They are then combined with machine learning algorithms like Support Vector Machines (SVM) for age classification or regression prediction.
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Deep Learning-based Methods: With the development of deep learning, Convolutional Neural Networks (CNN) have been widely applied to face age detection. CNNs can automatically learn multi-level, abstract features from facial images, more effectively capturing age-related facial feature changes. For example, CNN models are trained on large datasets of labeled facial images to learn the mapping relationship between facial images and age, enabling age prediction for unknown facial images.
Running Example
After successfully connecting via SSH, enter the following command to operate OriginMan:
After launching, you will see the following interface:
You can now open a browser and check the IP address behind OriginMan. Enter IP:8000 in the page, for example: http://192.168.127.10:8000/TogetheROS/
