Image analysis – Applications – Personnel identification
Reexamination Certificate
1998-07-20
2001-09-18
Mehta, Bhavesh (Department: 2621)
Image analysis
Applications
Personnel identification
C382S107000, C382S165000, C382S170000, C382S190000, C382S257000
Reexamination Certificate
active
06292575
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to systems for identifying an individual, and in another case, verifying the individual's identity to perform subsequent tasks, such as allowing access to a secured facility or permit selected monetary transactions to occur.
Modern identification and verification systems typically provide components that capture an image of a person, and then with associated circuitry and hardware, process the image and then compare the image with stored images, if desired. In a secured access environment, a positive match between the acquired image of the individual and a pre-stored image allows access to the facility.
The capture and manipulation of image data with modem identification systems places an enormous processing burden on the system. Prior art systems have addressed this problem by using Principal Component Analysis (PCA) on image data to reduce the amount of data that needs to be stored to operate the system efficiently. An example of such a system is set forth in U.S. Pat. No. 5,164,992, the contents of which are hereby incorporated by reference. However, certain environmental standards need still be present to ensure the accuracy of the comparison between the newly acquired image of the pre-stored image. In particular, the individual is generally positioned at a certain location prior to capturing the image of the person. Additionally, the alignment of the body and face of the individual is controlled to some degree to ensure the accuracy of the comparison. Lighting effects and other optical parameters are addressed to further ensure accuracy. Once the individual is positioned at the selected location, the system then takes a snapshot of the person, and this still image is processed by the system to determine whether access is granted or denied.
The foregoing system operation suffers from a real time cost that slows the overall performance of the system. Modem system applications require more rigorous determinations in terms of accuracy and time in order to minimize the inconvenience to people seeking access to the facility or attempting to perform a monetary transaction, such as at an automated teller machine (ATM). Typical time delays in order to properly position and capture an image of the person, and then compare the image with pre-stored images, is in the order of 3 to 5 seconds or even longer. Consequently, these near real-time systems are quickly becoming antiquated in today's fast paced and technology dependent society. There thus exists a need in the art to develop a real-time facial identification and verification system that in real-time acquires and processes images of the individual.
Accordingly, an object of this invention is to provide a real-time identification and verification system.
Another object of this invention is to provide an identification system that simplifies the processing of the acquired image while concomitantly enhancing the accuracy of the system.
Other general and more specific objects of the invention will in part be obvious and will in part appear from the drawings and description which follow.
SUMMARY OF THE INVENTION
The present invention provides systems and methods of a facial recognition system for acquiring, processing, and comparing an image with a stored image to determine if a match exists. The facial recognition system determines the match in substantially real time. In particular, the present invention employs a motion detection stage, blob stage and a color matching stage at the input to localize a region of interest (ROI) in the image. The ROI is then processed by the system to locate the head, and then the eyes, in the image by employing a series of templates, such as eigen templates. The system then thresholds the resultant eigenimage to determine if the acquired image matches a pre-stored image.
This invention attains the foregoing and other objects with a system for refining an object within an image based on color. The system includes a storage element for storing flesh tone colors of a plurality of people, and a defining stage for localizing a region of interest in the image. Generally, the region is captured from a camera, and hence the ROI is from image data corresponding to real-time video. This ROI is generally unrefined in that the system processes the image to localize or refine image data corresponding to preferred ROI, such as a person's head. In this case, the unrefined region of interest includes flesh tone colors. A combination stage combines the unrefined region of interest with one or more pre-stored flesh tone colors to refine the region of interest based on the color. This flesh tone color matching ensures that at least a portion of the image corresponding to the unrefined region of interest having flesh tone color is incorporated into the refined region of interest. Hence, the system can localize the head, based on the flesh tone color of the skin of the face in a rapid manner. According to one practice, the refined region of interest is smaller than or about equal to the unrefined region of interest.
According to one aspect, the system includes a motion detector for detecting motion of the image within a field of view, and the flesh tone colors are stored in any suitable storage element, such as a look-up-table. The flesh tone colors are compiled by generating a color histogram from a plurality of reference people. The resultant histogram is representative of the distribution of colors that constitute flesh tone color.
According to another aspect, a blob stage is also employed for connecting together selected pixels of the object in the image to form a selected number of blobs. This stage in connection with the motion detector rapidly and with minimal overhead cost localize a ROI within the image.
According to another aspect, the system when generating the flesh tone colors employs a first histogram stage for sampling the flesh tone colors of the reference people to generate a first flesh tone color histogram. The color is then transformed into ST color space. The system can also optionally employ a second histogram stage for generating a second color histogram not associated with the face within the image, and which is also transformed into ST color space.
According to still another aspect, the system comprises an erosion operation to the image data corresponding, for example, to a face, to separate pixels corresponding to hair from pixels corresponding to face, as well as to reduce the size of an object within the image, thereby reducing the size of the unrefined region of interest.
According to yet another aspect, the system also performs a dilation operation to expand one of the region of interests to obtain the object (e.g., face or eyes) within the image.
The present invention also contemplates a facial recognition and identification system for identifying an object in an image. The system includes an image acquisition element for acquiring the image, a defining stage for defining an unrefined region of interest corresponding to the object in the image, and optionally a combination stage for combining the unrefined region of interest with pre-stored flesh tone colors to refine the region of interest to ensure at least a portion of the image corresponding to the unrefined region of interest includes flesh tone color. The refined region of interest can be smaller than or about equal to the unrefined region of interest.
According to another aspect, the system also includes a detection module for detecting a feature of the object.
According to another aspect, the combination stage combines a blobs with one or more of flesh tone colors to develop or generate the ROI.
According to another aspect, the system further includes a compression module for generating a set of eigenvectors of a training set of people in the multi-dimensional image space, and a projection stage for projecting the feature onto the multi-dimensional image space to generate a weighted vector that represents the person's feature corresponding to the ROI. A d
Bortolussi Jay F.
Cusack, Jr. Francis J.
Ehn Dennis C.
Kuzeja Thomas M.
Saulnier Michael S.
Lahive & Cockfield LLP
LAU Technologies
Laurentano Anthony A.
Mehta Bhavesh
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