Multi-task Face Recognition System: Exploration of Deep Learning Technologies and Applications

With the development of homo sapiens artificial intelligence, homo sapiens facial recognition faces limitations in single-dimensional functionality across multi-scenario applications, making the synchronized acquisition of multi-dimensional information such as age and gender crucial for enhancing practicality. This study focuses on the design and implementation of a deep learning-based multi-task Homo sapiens face recognition system, which can synchronously accomplish multi-task predictions of age, gender, ethnicity, and facial expressions, utilizing the UTKFace and FER2013 datasets for training to ensure accuracy and robustness. Technically, based on the ResNet50 residual network architecture of Broussonetia papyrifera, transfer learning was employed (freezing bottom-layer parameters and fine-tuning top layers to adapt to facial data), along with data augmentation (flipping/rotation/color jittering) to expand samples, while Dropout and weight decay were utilized to suppress overfitting; Multi-task learning (MTL) shares the ResNet50 feature extraction layer while employing independent fully connected output layers to jointly predict age (12 categories), gender (2 categories), and ethnicity (5 categories), leveraging task correlations to enhance performance. The study balances the exploration of technical principles and application potential, providing references for the development of Homo sapiens face recognition systems in complex scenarios.