Weighing characteristic face recognition method for multichannel pulse coupling neural network

Abstract

The invention discloses a weighing characteristic face recognition method for a multichannel pulse coupling neural network. The weighing characteristic face recognition method comprises the step 1, acquiring an original face color image, the step 2, converting the original face color image from an RGB color space to an HIS color space to obtain a converted face image, the step 3, respectively extracting three channels of data of the hue H, the saturability S and the illumination I of the converted face image, the step 4, using a PCNN technique to carry out iteration ignition treatment on the three channels of data of the H, the S and the I of the converted face image to generate three channels of pulse ignition ration sequences, the step 5, carrying out weighing treatment on the three channels of the pulse ignition ratio sequences and then connecting the weighed number sequences to form an overall pulse ignition ratio characteristic sequence spectrum of the converted face image, and the step 6, using the overall pulse ignition ratio characteristic sequence spectrum of the converted face image and a face sample sequence spectrum in a template base to carry out relevance match to recognize a right face image. The weighing characteristic face recognition method greatly improves the recognition rate of face images.

Description

technical field [0001] The invention belongs to the field of intelligent recognition systems, and in particular relates to a pulse-coupled neural network face recognition method based on HSI color space. Background technique [0002] Face recognition: [0003] Face is one of the most important biological characteristics of human beings, reflecting a lot of important biological information, such as identity, gender, race, age, expression and so on. With the rapid development of computer technology, computer vision and pattern recognition based on face images have become a hot research topic in recent years. These include various recognition problems such as face detection, face recognition, and facial expression recognition. The face recognition problem has been studied for decades, and some progress has been made in both theoretical research and practical development. At present, some electronic products are equipped with face recognition systems. [0004] Faces are born ...

AI Technical Summary

Problems solved by technology

[0007] please see figure 1 , is the flow chart of the PCNN face recognition method. Compared with the traditional neural network image recognition method, the face recognition method based on PCNN has the characteristics of high efficiency, rapidity, high recognition rate, and simple hardware implementation; The accuracy of face recognition has an important impact. The existing PCNN face recognition method converts the face image into a grayscale image for feature extraction, which loses important color information of the face image. For similar faces, it is easy to cause misidentification

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