A Facial Recognition Approach for Psychology

Abstract

The invention belongs to the technical field of facial recognition, and discloses a facial recognition method suitable for psychology, which includes collecting images to obtain high-definition images; recognizing and segmenting facial images from the high-definition images; Processing; Generate the feature vectors to be identified of the face detail characteristics; pre-store the feature vectors of known faces by the face feature library module; match the output feature vectors to be identified with all feature vectors in the feature library Calculate and output the identity result corresponding to the feature vector to be identified; display the collected image information and the matching result information. The present invention constructs the face feature database through the face feature library module, and performs fast matching through the feature matching module, so that the identity of the face image to be recognized can be judged in real time on a system with relatively limited computing resources, and the time-consuming is short and the accuracy rate is high .

Description

Technical field [0001] The present invention belongs to the field of facial recognition, and more particularly to a face recognition method suitable for psychology. Background technique [0002] At present, the existing technology in the industry is like this: [0003] Facial recognition - face recognition, referred to as face recognition, face image recognition, face recognition, etc., facial recognition uses a universal camera as an identification information acquisition device. The facial image of the identification object is acquired in contact with contact, and the computer system completes the identification process after acquiring the image. However, the existing surface recognition is not accurate to the facial image, affecting the identification accuracy of the facial part; and the surface recognition efficiency is low, time consumption. [0004] Cognition and understanding of graphs is an important basis for human beings to acquire external information and make judgmen...

AI Technical Summary

Problems solved by technology

Although a certain degree of efficiency has been achieved, there are still some shortcomings: the matching of the algorithm implementation process and visual resolution is not intuitive; the algorithm is complex, resulting in a large amount of data processing and high operating costs; the average analysis of the algorithm leads to important geometric features in the graph. The impact of changes in the overall similarity, resulting in a certain deviation in accuracy and stability

[0007] Existing face recognition is not accurate in extracting facial images, which affects the accuracy of face recognition; at the same time, face recognition is inefficient and time-consuming

[0008] In the existing graphic segmentation, the common recognition method of similarity does not match intuitively with the matching of visual resolution, and the algorithm is complex, resulting in a large amount of data processing, high operating costs, and a certain deviation in accuracy and stability.

[0009] The data processing accuracy of the existing technology is poor

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