Face recognition method and system based on adaptive score fusion and deep learning

Abstract

The invention discloses a face recognition method and system based on adaptive score fusion and deep learning. The system comprises a near infrared face and visible light face acquisition unit for jointly acquiring pictures of the same face by cooperation of cameras of two modes, a face feature extraction unit for respectively extracting features from the acquired near infrared face picture and visible light face picture via a deep convolution model, an adaptive score fusion unit for solving the similarity (that is, the score of the pictures) of the two pictures via an Euclidean distance and then fusing the face features extracted by using a deep learning algorithm via an adaptive score fusion algorithm on the score level to solve the final score of the user, and a result output unit for outputting the final score. The method and the system can solve the influence of the intensity of near infrared light on face imaging, and can also solve the problem that the details of the near infrared picture disappear by using the visible light picture.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a face recognition method and system based on adaptive score fusion and deep learning. Background technique [0002] At present, face recognition research has achieved a lot of results. In recent years, face recognition algorithms based on deep learning are significantly better than other face recognition algorithms in terms of feature expression ability. Therefore, face recognition algorithms based on deep learning Recognition algorithms are widely used in engineering projects, but in practical applications, face recognition algorithms based on deep learning have great defects and deficiencies in complex lighting environments, which greatly limit deep learning. The scope of application of the algorithm. [0003] In order to overcome the impact of environmental lighting changes on the performance of deep learning algorithms, academia and related companies have done a lot of resea...

AI Technical Summary

Problems solved by technology

Since near-infrared images are clear and frontally illuminated under any ambient light, this provides a good image data technology for building a face recognition system based on deep learning that is not affected by ambient light and is highly accurate. The problem of ambient lighting in the field of face recognition overcomes the disadvantages of visible light technology that degrades performance after light changes and cannot be recognized in dark situations

[0006] However, due to the defect of the imaging principle of the near-infrared camera, the facial features extracted by the deep learning algorithm cannot express some detailed features of the human face.

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