Age Prediction
Explore how to estimate age from facial images by applying DeepFace and MediaPipe libraries in Python. Learn the key steps including face detection, bounding box enlargement, and age classification using convolutional neural networks. Understand the challenges affecting accuracy and practical applications in predictive analytics.
Introduction
Age is a fundamental characteristic of human beings. Age estimations are based on disparate patterns that emerge from facial appearance. Recently, age estimation has served as a vital attribute in various real-world applications such as these:
- Access control and visual surveillance.
- The human-computer interaction (HCI).
- Marketing intelligence, for instance, the AI product Quividi can enable face and context-responsive advertisement campaigns.
However, the estimation of age from a face image remains challenging since it can be impacted by various factors. These factors include variation in lighting, scale, occlusion, and differences in the age presentation of certain individuals, especially teens and young adults.
Recently, many ML algorithms based on convolutional neural networks (CNNs) models were substantially used for automatic age estimation and classification. These algorithms improved their efficiency and accuracy in this domain. Basically, CNN is a deep learning ...