Performance optimization of multi-task convolutional neural networks for face recognition
This study investigates the performance of a multi-task convolutional neural network for face recognition.The model employs three independent task networks dedicated to face detection,keypoint localization,and face recognition,respectively.These three networks share the features of the underlying convolutional layers during the training process,enabling the model to simultaneously learn multiple tasks and thereby improving the generalization capability and recognition accuracy.To enhance the model's learning ability for images,data augmentation and transfer learning techniques are proposed.These techniques significantly enhance the accuracy,robustness,and reliability of the face recognition system.The research results offer valuable insights for the further development of face recognition technology,particularly in addressing complex scenarios and diverse facial images,opening up potential applications.
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