机器仿生眼的多任务学习人脸分析

The performance of human-machine interaction is crucial for intelligence robot, and face analysis makes human-machine interaction more friendly. In this paper, a multi-task learning convolutional neural network is proposed. The tasks of smile recognition and gender classification are solved simultaneously. Inherent correlated tasks are learned, and the performance of individual task is improved. On CelebA test dataset, the proposed network achieves high accuracy on a smile recognition task and a gender classification task. The proposed model is tested on the designed machine bionic vision eyes, achieving satisfactory result on smile recognition and gender classification. The research on face analysis in this paper improves the human-machine interaction ability with the machine bionic eyes.