Research on Face Recognition Algorithm of Intelligent Elderly Care Based on Machine Learning

With the aging of the population, smart elderly care is becoming more and more important. Nowadays, with the rapid development of social machine learning technology, machine learning technology is widely used in various research fields, making many fields have breakthrough progress, especially in the field of computer vision. Among them, image style transfer and face recognition technology are widely used in computer vision. Facing the field of intelligent elderly care, this paper studies the facial expression recognition algorithm of intelligent elderly care facial recognition based on machine learning, which can provide partial data support for judging whether the elderly are depressed through the expression recognition of the elderly. In this paper, style transfer technology in machine learning is applied to facial expression recognition and face recognition. Since a person's facial expression can be decomposed into expression components and neutral components, this paper proposes a facial expression recognition method based on style transfer. This method obtains different generators by training cycles to uniformly generate adversarial networks (Cycle-GAN). These different generators migrate different expressions to neutral, so each generator corresponds to a different expression. In the test phase, input the expression images into the above-trained generator. Since only the generator corresponding to the input expression can migrate to a neutral expression, facial expression recognition can be achieved in this way. The experimental results show that the method not only has outstanding performance in the facial expression data set obtained under laboratory conditions, but also has a very high recognition rate in the facial expression data set obtained under natural conditions, which can be used in the facial expression recognition of intelligent elderly care.