I2DKPCN: an unsupervised deep learning network

Zhao, Ruyi; Shi, Fanhuai

doi:10.1007/s10489-021-03007-9

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…Srivastava et al used deep learning to build a hybrid model to improve the accuracy of personal face recognition involving violence [19]. Zhao et al aimed at the problem that face recognition takes too long; they reduced the time consumption in face recognition by building an unsupervised deep learning network [20]. Vedantham has constructed a classifier by using depth learning to improve the accuracy of facial expression recognition [21].…”

Section: Related Workmentioning

confidence: 99%

Research on Image Algorithm for Face Recognition Based on Deep Learning

2023

IJACSA

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As people's requirements for applications are getting higher and higher, the recognition of facial features has been paid more and more attention. The current facial feature recognition algorithm not only takes a long time, but also has problems such as large system resource consumption and long running time in practical applications. Based on this, the research proposes a multi-task face recognition algorithm by combining multi-task deep learning on the basis of convolutional neural network, and analyzes its performance in four dimensions of face identity, age, gender, and fatigue state. The experimental results show that the multi-task face recognition algorithm model obtained through layer-by-layer progression takes less time than other models and can complete more tasks in the same training time. At the same time, comparing the best model M44 with other algorithms in four dimensions, it is found that the Mean Absolute Error lowest is 3.53, and the highest Accuracy value is 98.3%. On the whole, the multi-task face recognition algorithm proposed in the study can recognize facial features efficiently and quickly. At the same time, its training time is short, the calculation speed is fast, and the recognition accuracy is much higher than other algorithms. It is applied to intelligent driving behavior. Analysis, intelligent clothing navigation and other aspects have strong practical significance.

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Section: Related Workmentioning

confidence: 99%