Foley-Sammon face recognition method fusing matrixes and vector feature extraction

A feature extraction and face recognition technology, applied in the field of Foley-Sammon face recognition, can solve problems such as time-consuming, large amount of calculation, and the existence of small samples.

Active Publication Date: 2017-09-22
吉安集睿科技有限公司
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Problems solved by technology

The disadvantage of the vector-based feature extraction method is that the image matrix must first be converted into a high-dimensional vector, resulting in a large amount of time-consuming face recognition processing and small sample

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  • Foley-Sammon face recognition method fusing matrixes and vector feature extraction
  • Foley-Sammon face recognition method fusing matrixes and vector feature extraction
  • Foley-Sammon face recognition method fusing matrixes and vector feature extraction

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Embodiment Construction

[0061] The invention proposes a bidirectional two-dimensional Foley-Sammon human face image feature extraction method, which compresses the human face image by using the bidirectional two-dimensional Foley-Sammon feature extraction method. Each compressed image is pulled into a vector by row and column, and then fused into a vector data. The identification information of the fused vector data is extracted to obtain the compressed data of the vector data. Finally, the nearest neighbor classifier is used to classify the results to realize the accurate recognition of face images. This method combines two feature extraction methods of matrix and vector, and has the advantages of small calculation, small storage space, and high recognition rate.

[0062] One embodiment of the invention is provided below:

[0063] In this embodiment, there are 10 people in the JAFFE facial expression database, and each person has 7 types of expressions, and each type of expression has 8 pieces. T...

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Abstract

The invention discloses a Foley-Sammon face recognition method fusing matrixes and vector feature extraction. According to the method, first, a two-dimensional Foley-Sammon identification vector matrix in the horizontal direction of a face image matrix is calculated, a two-dimensional linear identification vector matrix in the vertical direction of the face image matrix is calculated, and then the two identification vector matrixes are utilized to realize bidirectional compression of face images; each compressed face image is pulled into vectors according to rows and columns, and then the vectors of each compressed face image are fused into one piece of vector data; identification information of the vector data obtained after fusion is extracted; and last, a nearest neighbor classifier is used to perform classification processing on a result to realize accurate recognition of the face images. Through the method, the Foley-Sammon method is adopted in the horizontal direction and the two-dimensional linear identification method is adopted in the vertical direction to extract the identification information, complementary identification feature information of the face images can be obtained, main feature information of the face images is reserved as much as possible, extraction of the identification information of the face images is more sufficient, and therefore the recognition rate of the face images is increased.

Description

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Application Information

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Owner 吉安集睿科技有限公司
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