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Human face recognition method

A technology of face recognition and recognition, which is applied in the field of face recognition, can solve the problems of large calculation scale, complex deep learning model, and inapplicability, and achieve the effect of less calculation, less number of samples, and improved face recognition efficiency

Active Publication Date: 2017-05-24
SHENZHEN LDROBOT CO LTD
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Problems solved by technology

However, the deep learning model in this algorithm is complex, and it is not suitable for all occasions if the calculation scale is large.

Method used

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Embodiment Construction

[0030] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0031] The embodiment of the present invention provides a face recognition method, please refer to figure 1 , a flow chart of the face recognition method provided in this embodiment, the method includes steps:

[0032] S10: Extract features from the key points in the collected face image of the user to be identified, and form a feature...

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Abstract

The invention discloses a human face recognition method. The method comprises the steps of extracting the features of key points in an acquired face image of a to-be-recognized user, and forming a feature vector by the features of key points; subjecting the feature vector and a training matrix to operation to generate a model, wherein the training matrix is composed of a covariance matrix formed through inputting the feature vector obtained through extracting the features of key points in the acquired face image of the user into a combined Bayesian model and then training the feature vector; comparing the model with human face images in a sample library, and then recognizing the user. According to the technical scheme of the human face recognition method, the features of key points in the acquired face image of the user are extracted to form the feature vector, and then the feature vector is subjected to model training and recognition through the combined Bayesian model. Compared with the current deep learning-based face recognition algorithm, fewer samples are required for model training, and the calculation amount is reduced. The face recognition efficiency can be improved.

Description

technical field [0001] The invention relates to the technical field of computer vision processing, in particular to a face recognition method. Background technique [0002] Face recognition is a biometric technology for identity authentication based on human facial feature information. By collecting images or video streams containing human faces, and detecting and tracking human faces in the images, the detected faces are matched and recognized. At present, face recognition has a wide range of applications, and it plays a very important role in many fields such as financial payment, access control and attendance, and identity recognition, bringing great convenience to people's lives. [0003] There are many face recognition methods, which can basically be attributed to face feature extraction combined with classification algorithms. Among all the algorithms, the face recognition algorithm based on deep learning can achieve better recognition results, and has attracted more...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V40/168
Inventor 李昂
Owner SHENZHEN LDROBOT CO LTD
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