Side face recognition method based on kernel standard correlation analysis
A technology of correlation analysis and face recognition, applied in the direction of character and pattern recognition, instruments, computer parts, etc., can solve problems such as inappropriateness, large amount of calculation, and inability to meet the application requirements of face recognition technology, so as to improve the recognition rate , the effect of quick recognition
Active Publication Date: 2017-10-24
HENAN INST OF ENG
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Although they can adapt to the diversity of gestures and have a high recognition rate, they still cannot avoid the disadvantages of large amount of calculation and the need for the cooperation of the recognized person.
Therefore, they are not suitable for the application in real-time monitoring that we are interested in. In practical applications, the image acquisition of the identified person is passive and cannot meet the application requirements of face recognition technology under current uncontrolled conditions.
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[0031] Example: see figure 1 , figure 2 , image 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 , and the present invention will be described in detail in conjunction with the accompanying drawings.
[0032] Profile Face Recognition Based on Kernel Canonical Correlation Analysis The proposed method for profile face recognition relies on the idea of learning a transformation that maps profile faces to their corresponding frontal faces using kernel canonical correlation analysis. Significantly improve the recognition rate by analyzing the non-linear nature of the mapping between object poses. This can be achieved by introducing appropriate kernel functions. The selection of the kernel is not straightforward, where some tests must be performed in order to select an appropriate kernel model and then tune its parameters.
[0033] In addition, faces are described using local binary model facial components instead of raw pixels: eyes, nose, mouth and chin. In the inv...
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The invention discloses a side face recognition method based on a kernel standard correlation analysis. The method includes: for the right side portion and the side face portion of a to-be-detected face image, manually noting the eyes, the nose, the mouth, and the chin; performing geometrical normalization processing with different degrees on the right side and side face attitudes, describing the face by employing a local binary pattern facial component, calculating 64 histograms for divided areas in a local binary pattern image, and connecting the histograms of 14 areas of the face to form a vector for description; and after extracting a local binary characteristic vector, performing dimension reduction operation through application of the standard correlation analysis, introducing non-linear generalization based on a kernel principal component analysis to consider the transformation non-linearity between a side face image and a right side face image, performing transformation of the right side or the side surface through mapping from an original input space to a high-dimensional characteristic space, calculating the Euclidean distance, and giving side face image identity information. According to the method, rapid recognition of the side face can be effectively realized in a non-restricted condition.
Description
Technical field: [0001] The invention relates to a safety monitoring of public space, in particular to a side face recognition method based on kernel specification correlation analysis. Background technique: [0002] Biometric identification is an active research area and has applications in security monitoring of various public spaces. Numerous biometric identification technologies, such as fingerprints, iris and palm prints, have been widely used in security monitoring systems. These characteristics allow to uniquely describe a person, however, in the field of public safety, it is difficult to identify and determine the identity of a person through these characteristics in the video stream as "biological" characteristics. Since 1970, face recognition technology has gradually improved in theory and technology, but it is still subject to the diversity of individual samples, such as lighting conditions, shooting angles, facial expressions, age changes, etc. In non-intrusive...
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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/172G06V40/168G06V10/50G06F18/2135
Inventor栗科峰熊欣郑吉玉王俊华王炜郝原
OwnerHENAN INST OF ENG