Method for identifying human faces based on HMM-SVM hybrid model

A technology of HMM-SVM and mixed model, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as difficult face recognition and difficult extraction

Active Publication Date: 2009-12-16
DALIAN UNIVERSITY
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Problems solved by technology

[0007] However, although humans can identify a person from their faces without difficulty, there are still many difficulties in using computers to perform fully automatic face recognition.
The main manifestations are: the face i

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  • Method for identifying human faces based on HMM-SVM hybrid model
  • Method for identifying human faces based on HMM-SVM hybrid model
  • Method for identifying human faces based on HMM-SVM hybrid model

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[0061] reference Figure 5 , It is a flowchart of the implementation steps of the present invention, and the implementation process of the present invention will be described in detail with reference to the figure. The embodiments of the present invention are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation procedures are given, but the protection scope of the present invention is not limited to the following embodiments.

[0062] The embodiment uses a public face database, the ORL face database of the University of Cambridge in the United Kingdom. The ORL library contains 400 112×92 face images of 40 people, with 10 faces per person. These images were taken at different times, with changes in posture, angle, scale, expression, and glasses. The specific face recognition process is as follows: 1. Image preprocessing

[0063] The preprocessing of the 112×92 face image mainly includes image smooth...

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Abstract

The invention discloses a method for identifying human faces based on an HMM-SVM hybrid model, which comprises the following steps: firstly, sampling human face images from top to bottom by sampling windows; extracting characteristic parameters of each sampling window image by respectively adopting discrete cosine transform (DCT) and singular value decomposition (SVD), and serially connecting the characteristic parameters into one-dimensional observation vectors; then, using the observation vectors of the training images of each human body to train the HMM model of each human body; adopting the Viterbi algorithm to calculate the output probability of the observation vectors of all images corresponding to each HMM model; and using the output probability to support the classified training and the identification test of a vector machine. Because each HMM model has good time sequence modeling ability, the numerical characteristics of each organ of a human face can be effectively combined by a state transfer model to more integrally describe the human face to support the excellent performance of the vector machine in the aspect of classification of limited samples.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a face recognition method, which is a method for face feature extraction and recognition in the field of biological feature recognition. Background technique [0002] Faces play an important role and significance in people's communication. In daily life, people use faces most to identify people around them. Due to the non-invasiveness of face recognition, it has the characteristics of directness, friendliness and convenience, and it is the most acceptable way of identification for people. With the widespread adoption of network technology and desktop video, image capture devices are becoming standard peripherals for personal computers. At the same time, the use of network resources such as e-commerce has put forward new requirements for identity verification. Face recognition has become the most promising. One of the means of biometric authentication. [0003] Among the vario...

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Application Information

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IPC IPC(8): G06K9/00
Inventor 张强周昌军魏小鹏
Owner DALIAN UNIVERSITY
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