Image analysis – Applications – Personnel identification
Patent
1993-03-04
1998-02-17
Couso, Yon J.
Image analysis
Applications
Personnel identification
382217, G06K 900
Patent
active
057199515
DESCRIPTION:
BRIEF SUMMARY
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to a method of processing an image and particularly, but not exclusively, to the use of such a method in the recognition and encoding of images of objects such as faces.
2. Related Art
One field of object recognition which is potentially useful is in automatic identity verification techniques for restricted access buildings or fund transfer security, for example in the manner discussed in our UK application GB9005190.5. In many such fund transfer transactions a user carries a card which includes machine-readable data stored magnetically, electrically or optically. One particular application of face recognition is to prevent the use of such cards by unauthorised personnel by storing face identifying data of the correct user on the card, reading the data out, obtaining a facial image of the person seeking to use the card by means of a camera, analyzing the image, and comparing the results of the analysis with the data stored on the card for the correct user.
The storage capacity of such cards is typically only a few hundred bytes which is very much smaller than the memory space needed To store a recognisable image as a frame of pixels. It is therefore necessary to use an image processing technique which allows the image to be characterised using the smaller number of memory bytes.
Another application of image processing which reduces the number of bytes needed to characterise an image is in hybrid video coding techniques for video telephones as disclosed in our earlier filed application published as U.S. Pat. No. 4,841,575. In this and similar, applications the perceptually important parts of the image are located and the available coding data is preferentially allocated to those parts.
A known method of such processing of an image comprises the steps of: locating within the image the position of at least one predetermined feature; extracting image data from said image representing each said feature; and calculating each feature a feature vector representing the position of the image data of the feature in an N-dimensional space, said space being defined by a plurality of reference vectors each of which is an eigenvector of a training set of images of like features.
The Karhunen-Loeve transform (KLT) is well known in the signal processing art for various applications. It has been proposed to apply this transform to identification of human faces (Sirovitch and Kirby, J. Opt. Soc. Am. A vol 4 no 3, pp 519-524 "Low Dimensional Procedure for the Characterisation of Human Faces", and IEEE Trans on Pattern Analysis and Machine Intelligence Vol 12, no 1 pp 103-108 "Application of the Karhunen-Loeve Procedure for the Characterisation of Human Faces"). In these techniques, images of substantially the whole face of members of a reference population were processed to derive a set of N eigenvectors each having a picture-like appearance (eigen-pictures, or caricatures ). These were stored. In a subsequent recognition phase, a given image of a test face (which need not belong to the reference population) was characterised by its image vector in the N-dimensional space defined by the eigenvectors. By comparing the image vector, or data derived therefrom, with the identification data one can generate a verification signal in dependence upon the result of the comparison.
However, despite the ease with which humans "never forget a face", the task for a machine is a formidable one because a person's facial appearance can vary widely in many respects over time because the eyes and mouth, in the case of facial image processing, for example, are mobile.
SUMMARY OF THE INVENTION
According to a first aspect of the present invention a method of processing an image comprises the step of modifying the image data of each feature to normalise the shape of each feature thereby to reduce its deviation from a predetermined standard shape of said feature, which step is carried out before calculating the corresponding feature vector.
We have found that recogniti
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Shackleton Mark Andrew
Welsh William John
British Telecommunications public limited company
Couso Yon J.
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