DE4406020C1 - Automatic digital image recognition system - Google Patents

Automatic digital image recognition system

Info

Publication number
DE4406020C1
DE4406020C1 DE4406020A DE4406020A DE4406020C1 DE 4406020 C1 DE4406020 C1 DE 4406020C1 DE 4406020 A DE4406020 A DE 4406020A DE 4406020 A DE4406020 A DE 4406020A DE 4406020 C1 DE4406020 C1 DE 4406020C1
Authority
DE
Germany
Prior art keywords
graph
stored
images
camera
image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
DE4406020A
Other languages
German (de)
Inventor
Wolfgang Dr Konen
Jan C Vorbrueggen
Rolf P Wuertz
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Idemia Identity and Security Germany AG
Original Assignee
Zentrum fur Neuroinformatik 44801 Bochum De GmbH
ZENTRUM fur NEUROINFORMATIK G
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zentrum fur Neuroinformatik 44801 Bochum De GmbH, ZENTRUM fur NEUROINFORMATIK G filed Critical Zentrum fur Neuroinformatik 44801 Bochum De GmbH
Priority to DE4406020A priority Critical patent/DE4406020C1/en
Application granted granted Critical
Publication of DE4406020C1 publication Critical patent/DE4406020C1/en
Anticipated expiration legal-status Critical
Application status is Expired - Fee Related legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G06K9/6203Shifting or otherwise transforming the patterns to accommodate for positional errors
    • G06K9/6206Shifting or otherwise transforming the patterns to accommodate for positional errors involving a deformation of the sample or reference pattern; Elastic matching
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/117Identification of persons
    • A61B5/1171Identification of persons based on the shapes or appearances of their bodies or parts thereof
    • A61B5/1176Recognition of faces
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00288Classification, e.g. identification
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00Individual entry or exit registers
    • G07C9/00126Access control not involving the use of a pass
    • G07C9/00134Access control not involving the use of a pass in combination with an identity-check
    • G07C9/00158Access control not involving the use of a pass in combination with an identity-check by means of a personal physical data

Abstract

The image recognition system uses Gabor filters of different size and orientation for extracting Gabor values which is combined via graphs which can be displaced, altered in scale and deformed, to allow comparison with stored graphs representing other images, using a graph comparison function. The position and scale of the graph are adjusted simultaneously in steps, to obtain the optimum comparison with the stored graph, before comparison of the values provided by each graph, their relative distance compared with a reference value to indicate a significant match.

Description

The invention relates to an automated image processing method for size and positionally invariant recognition of intrinsically deformable objects, in particular face you.

For several years, in the field of image processing methods are known allow recognition of objects from individual views (images) of these objects. These methods are characterized by a more or less strong robustness Variations of object views (position, orientation in space, size, object intrinsic Distortions, lighting). In particular, numerous works deal with the difficult problem of recognizing faces, which is a class of objects with numerous intrinsic deformation degrees of freedom (facial expressions).

In the numerous works in relevant trade journals, dealing with the Erken employment of faces and published in relevant journals the following solutions are suggested:

  • 1. Bichsel and Seitz, DAGM, pp. 106-113, Springer-Verlag 1990 have proposed Images extracted features a neural network (multi-layer perceptron) too respectively. A very similar approach is described in Bouattour, Fogelman-Soulie et al., Artificial Neural Networks 2, p. 456, Elsevier-Verlag 1992. In Lampinen, Artificial Neural Networks, p. 328, Elsevier-Verlag 1991 becomes a feature classifi cation uses factor analysis and a neural Kohonen network. The disadvantage all of these methods is that for each new person to be recognized a whole series of Views must be recorded and that the neural networks then again have to be trained. In addition, results of these methods are in the form  Detection rates reported only for small databases (3-20 people). It It must be assumed that the recognition rate increases with people number decreases.
  • 2. From Lades et al., IEEE Transaction on Computers, 42, 300-311, 1993 [Lades'93] an approach is known in which the comparison between to be recognized and ge stored image is realized by a flexible imaging mechanism, where the best possible mapping is determined by an optimization method. This method is very efficient in terms of position, orientation in space and object intrinsic distortions, furthermore, are also in large numbers of people (<100) achieved high detection rates. However, it has the disadvantage that there is no size invariant detection allowed.
  • 3. An interesting method, which is also a mapping between images through an optimization method is realized by M. Shackelton, patent WO 93/15475, 1993 [Shackelton'93]. The procedure takes place in pictures auto matically prominent features and connects them to a net-like structure tur (template). Such templates can be used in principle, similar Compare images with each other and can thus also for comparison of Gesich tern be used. However, there will be no more in [Shackelton'93] Information on how such a comparison of different images of the same Ob projects in detail and which quality of the classification can be achieved can be.

With regard to passenger access controls, there are no commercial machines in the prior art applications that require the automated, location and size-invariant detection of Use faces as a control feature.

For a better understanding of the present invention will be in the following the state The technique as specified by [Lades'93] is shown in more detail. To who the first some terms and notations essential to the invention summarized:

It is state of the art, as described, for example, in the work of D.J. Field, Journal Opt. Soc. At the. A4, pp. 2379-2394, 1987, that for feature extraction from images Filter operations (mathematically: convolutions) with certain filter masks used become. One particular class of filter masks are the Gabor filters. Various Gabor filters differ in size and orientation in the image plane:

  • A Gabor feature denotes the result of folding the image on a be tuned pixels with a specific Gabor filter.
  • Further, when an image has been convolved with k different Gabor filters, a jet the entirety of all gabor features at a given pixel in the jet to a k-component vector are summarized.  
  • - A regular graph is the summary of m × n jets in a two-dimensional grid-like arrangement (see Figure 1a). Each jet is associated with one of the nodes of the grid; adjacent nodes are linked by horizontal and vertical connections. For object images to be stored, the information about an object image is stored in the form of regular graphs.
  • - An irregular graph also consists of m × n nodes, which are linked in the same topology as the regular graph, but here the nodes are not necessarily on a rectangular grid, but are arbitrarily arranged in the image plane (see Figure 1b). The nodes are each assigned the jets that belong to the pixel on which the node comes to rest.
  • - The horizontal and vertical connections between adjacent nodes also referred to as links.
  • - To evaluate the similarity of two graphs (regular or irregular), a graph comparison function E is introduced: E = E sim + λE top (1) This function assumes lower values the more similar two graphs are. The first term E sim evaluates the similarity of the jets to respective corresponding nodes of the two graphs by comparing the corresponding Gabor features with one another (negative cosine of the angle between the two jets); high similarity means low values of E sim . The second term E top evaluates the topological similarity of both graphs by summing up the amounts of the difference vectors of respective corresponding links. (An illustrative model is to think of the links as mechanical springs, which in their rest positions form the first of the two graphs, and E top is proportional to the work that must be applied against the spring forces to link them to the topology of the graphite to cover up the second graph.)
  • When comparing a new object image with N stored objects, N values of the graph comparison function E₁ <E₂ <are obtained. , , <E N , and σ E denote the mean and standard deviation of the data (E₂, ..., E N ), and object 1 is the most similar object in terms of the comparison function. The similarity is referred to as significant and we speak of significant recognition, if for fixed parameters s₁, s₂ at least one of the criteria (E₁ -) / σ E <s₁ or (E₁-E₂) / σ E <s₂ is satisfied. Otherwise, we are talking about insignificant detection, and the new object is rejected as being dissimilar to all stored objects.

In [Lades'93] a method is now described which describes Gabor features of various kinds Orientation and scaling are extracted from an image and these are in shape for each pixel of jets. For an object image to be saved, the jets become one regular graph centered on the object. In a new one Image is searched for a graph optimally matching the stored graph. there The new graph at its nodes contains the jets that go to the pixel at the location of the node Nodes belong. Its position and shape is optimized in a two-phase process:

  • 1. Shift the graph as a whole (Global Move) until an optimal state is found that will.
  • 2. Starting from the optimal state of 1. individual nodes of the graph ver pushed (local distortion).

In both phases, the optimization proceeds as follows: It becomes a random one Shift vector selected and on the graph or a node of Gra phen applied. The state of the thus changed graph is determined by means of the graph Comparison function E rated. Only if the new state is a cheaper value for E has, the shift is accepted.

Both phases terminate if no improvement is found in V max consecutive shift steps. The result of the optimization is the final value of the graph comparison function E. The optimization is repeated for all stored graphs, and a sequence of values E₁ <E₂ <E₃ <is obtained. , , on the basis of which it can be determined whether there is a significant recognition of object 1 .

However, the method described has the following disadvantages:

  • - It is not size invariant because the graph is only shifted, not scaled. It has been previously reported in the literature (Buhmann et al., IJCNN II, 411-416, IEEE, San Diego, 1990) assumed that when scaling the graph, the fil coefficients must be interpolated or extrapolated accordingly. Such Method is computationally intensive, because after each optimization step interpolated who must. Furthermore, it has been assumed that the size determination only through a hierarchical process that ranges from coarse to fine resolution progresses, succeeds. The invention described here solves the problem of sizes invariant recognition through a significantly simplified procedure.
  • - The search effort grows linearly with the number of stored objects. Further the correct recognition becomes more and more difficult as the number of objects increases. This makes use in large databases problematic. The here darge invention describes a new method, the verification, which the above Disadvantages avoids. This method is also very good with combine the size-invariant detection.

The already known from the prior art methods have a number of Disadvantages and can not satisfy in every way. It therefore exists a constant need for improved methods of detecting intrinsically deformable Objects.

Object of the present invention was a comparison with the prior art of the Technology to provide improved method for detecting objects. The aim was to create a way to differentiate to the effect that different pictures of the same object (with differences in position, view and size of the object) are recognized as similar while images of different objects classified as dissimilar.  

Another objective was a technical use of this method to develop as a personal access control system, based on an improved procedure based on recognition of faces. The task is in particular the, Verification of access authorization even with a large circle of authorized persons to perform quickly and safely.

Surprisingly, it has been found that the size-invariant object recognition simply by significantly improving that one the Global Move simultaneously with a Global Scale connects by looking at the graph in each optimization step shifts and scales with a factor close to 1. An interpolation of the filterko Efficient is contrary to expectation for a wide range of object sizes (of about 60% to 140% of the original size) is not necessary. With that you have one opposite Existing solutions accelerated adaptation to resizing, as was the case for many Applications of particular importance are.

The subject of the present invention is a method for automated recognition objects from images of these objects, using a digital image with Gabor's Filtering different size and orientation Gabor features extracted in one shift, scaling and deformable graph G are summarized. For each to optimize the shape and position of the graph G to the stored graph, that the graph comparison function E assumes optimal values. The discovery of the For each of the stored graphs, the optimal graph G is divided into two phases by.

Phase 1

Selection of a random displacement vector and scaling factor for the entire graph. The graph thus changed in the new image is displayed via the graph Comparison function E compared with the stored graph, with the links of the previously stored graphs are multiplied by the scaling factor. Just if the new state has a more favorable value for E, the change in the Graphene accepted.

Phase 2

All links of the stored graph are multiplied by the optimal scaling factor determined in Phase 1. Starting from the optimal state of the new graph determined in phase 1, individual nodes of this graph are shifted (local distortion) until an optimal irregular graph is found ( Figure 2).

In a preferred embodiment of the present invention, in each Opti Stage 1, based on the current graphene location and size, at random a shift of up to 3% of the image size and at the same time a scaling of the graph is chosen up to 10% of the graph size. However, the procedure delivers in the examined images in a wide range of parameters approximately the same good results.

With this embodiment, for images, the stored objects in a order 75% smaller scale, showing the right scaling factors with accuracy  of ± 2%. This was shown by experiments in which 12 Gabor filters (4 equidistant Orientations with 3 different sizes, characterized by their optimal freeze f, which, starting at the highest frequency of f = π / 2, at a distance of half octaves). The 12-component jets are in ei arranged on a 7 × 10 graph and as image material face images (128 × 128 Pixel) used by persons in front of white background, as depicted in [Lades'93] are. Under these conditions is different starting positions and sizes of the graph G from the optimal graph size (ie here by 75% smaller) with a Reproducibility approached by ± 2%.

In a further embodiment of the invention, the optimization of the graph G only once with respect to one of the stored graphs (reference graph) and remains restricted to Phase 1. The optimal graph thus obtained G then becomes without further optimization steps with all stored graphs compared. Surprisingly, it was found that despite the drastically reduced Op Almost constant good recognition performance can be achieved. These Embodiment has the advantage that it can be carried out much faster and thus also suitable for use on large databases of stored graphs is.

In addition to detection (finding an object among a number of stored object pictures), the method is also particularly good for verification, d. H. to the decision tion, whether a given object image with a specific stored object image B (whose graph is used as a reference graph) matches. This will be the case given picture O except with the stored picture B also with N randomly selected compared to other stored images to decide if the similarity between B and O is significant. N is a fixed, not the total size of the database dependent number, so that in this method the computational effort is independent of the size the database is. The comparison with N further stored images offers the advantage that differences caused by a changed shooting situation at the given picture (eg caused by other lighting), be separated from differences in the Objects themselves.

Especially for this verification task, the embodiment described above is suitable form, in which, to reduce the computational effort, the optimization can only be achieved with one Re graphs (namely B) and is already terminated after phase 1, especially good.

This is shown by the following test results: 88 object images of faces with about 20 ° head turned to the side are looking straight ahead with 100 stored images People compared. In one embodiment of the invention, 40 Gabor filters are used (8 equidistant orientations each with 5 different sizes, characterized by their Optimal frequencies f, which, starting at the highest frequency of f = π / 2, in the distance of half octaves) and 7x10 graphene. The thresholds s₁ and s₂ are set so that object images are 100% rejected when the associated stored image (temporary) is removed from the database. Will that be now  associated stored image reinserted, then arise with the same thresholds following rates of correct and significant detection (N = 100):

Detection, with phase 1 and 2 | 84% Verification, with phase 1 and 2 93% Verification, only phase 1 91%

In an attempt to size-invariant verification in which 88 object images with 20 ° Head rotation replaced by 89 images of faces in reduced size (75%), results in a verification rate (Phase 1 only) of 83%.

Overall, the method thus allows automated detection or Verifika tion of objects from digital images of these objects using Gabor Characteristics which are displayed on a shiftable, scalable and deformable grid, the Gra phen, whose optimal shape through the two-phase minimization of Graphene comparison function is determined. In the first phase, both size and also optimized the position of the graph simultaneously.

For many applications of visual object recognition, the invariance plays against size and position changes of objects have a special role. For example Personal access control with automatic camera ("electronic gatekeeper") only then usable in practice, if distance and position of the person in front of the camera certain limits are variable. The method described here is particularly suitable good for such an application, especially as a distinction in a large number of Persons is possible. Compared to a human porter, the automated Method the benefits of greater objectivity, reproducibility and fatigue.

For personal access controls, high safety standards must be adhered to. The method described here offers the advantage of a high level of security in the Instruction of unauthorized persons (100% on the test data). This one described The procedure is also particularly good when used in conjunction with others Access control measures (eg code cards) is used. Through this Kombina tion, the following advantages arise:

  • - An unjustified person who obtains a code card or one fakes such, can not gain access through the code card alone. This leads to an increase in security.
  • - By the code card information can be specified which of the stored faces is recognizable. The automatic detection is reduced to the Task of the automatic verification, whether the current image has a significant resemblance having the stored image. In contrast to the detection is the  Computing effort for verification regardless of the size of the database; the The method can thus also be used for very large databases.

Personal access control is just one example of the technical applicability of the Face recognition method. More generally, a method in which a Ka mera a facial image is taken and compared with stored images, for use non-contact identity verification. This can be used to per to make personal settings on a technical device (for example Driver's seat setting in the car) or personal access rights to a techni to give the device.

Particularly advantageous is such a non-contact control method in combination nation with other identification features for verification. It is meaningful according to the same advantages as for verification in person access controls.

The method described can also be advantageous for the determination of the similar speed between object images. As a measure of similarity serves at the end of Optimization obtained value of graphene comparison function. This can be great Automatically search databases for similar objects. Because only these for one further (human) assessment, this means one considerable time savings. The method is also particularly suitable for the like between phantom images of faces and stored face photographs determine.

Finally, the method can also be used for automated visual quality control use. In production processes one is often faced with the task that the correspondence mung currently existing parts with the specifications must be checked (example Assembly of electronic boards). Often the automation of the visual fails Testing for the lack of robustness of the methods used. The one described here Method can be used particularly advantageous if in their fundamental Structure-related production parts must be kept safe apart.

Claims (9)

1. A method for automated recognition of objects from images of these objects, wherein extracted from a digital image with Gabor filters of different size and orientation Gabor features that are combined in a shift, scalable and deformable graph G and with stored graphs of other images are compared by a graph comparison function E, consisting of a similarity of the Gabor characteristics evaluating proportion and a form preservation of Gra phen-assessing proportion, is calculated, characterized in that by stepwise and simultaneous changes of Position and size of the graph G optimally sets this (phase 1) compared to a stored reference graph and uses this G to determine the values of E for each of the stored graphs, the best value distance from the remaining E values taking a decision about it allows a significant detection.
2. The method of claim 1, wherein the optimization of the graph G extended in phase 1 so that for each of the stored graphs Gradual changes in form (local distortions) that lead to an improvement tion of the graph comparison function E (phase 2).
3. The method of claim 1 or 2, wherein in each optimization step Phase 1 happens to be a shift of up to 3% of the image size and one Scale the graph up to 10%.
4. The method according to any one of claims 1 to 3, wherein the sizes of the inserted Gabor filters set so that their respective best frequencies in the distance of half-octaves and at least three different sizes used.
5. Device for non-contact identity verification, consisting of camera and Computer, characterized in that a recorded by the camera Face image by one of the methods mentioned in claims 1 to 4 automatically compared with all stored facial images of people becomes, and with a significant recognition of one of the stored persons a personal action is triggered.
6. People access control, consisting of camera, computer and electric Access unlocking, characterized in that a through the camera taken face image by one of the claims 1 to 4 mentioned procedures with all stored face images of authorized users People is compared automatically, and at a significant detection one of the stored faces the access release is actuated.
7. People access control, consisting of camera, computer, electric Access unlocking and a further independent control device, by which the identity of the person to be recognized is given, thereby  characterized in that this identity is verified by the associated ge saved face image B with a current shot taken by the camera len face image according to one of the methods mentioned in claims 1 to 4 and, if the significance is positive, activates the access release becomes.
8. Method for database search in image databases, characterized that in a submitted image according to one of the claims 1 to 4 described procedures all sufficiently similar images in the database which are distinguished by the fact that for them the graphene Compare function takes lower values than for all other pictures.
9. Automated visual quality control in the production process, consisting from camera, computer and secretion mechanism, characterized records that the currently produced parts over one of the claims 1 to 4 described methods are compared with nominal values and at Presence of a discrepancy of the screening mechanism is actuated.
DE4406020A 1994-02-24 1994-02-24 Automatic digital image recognition system Expired - Fee Related DE4406020C1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
DE4406020A DE4406020C1 (en) 1994-02-24 1994-02-24 Automatic digital image recognition system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE4406020A DE4406020C1 (en) 1994-02-24 1994-02-24 Automatic digital image recognition system

Publications (1)

Publication Number Publication Date
DE4406020C1 true DE4406020C1 (en) 1995-06-29

Family

ID=6511121

Family Applications (1)

Application Number Title Priority Date Filing Date
DE4406020A Expired - Fee Related DE4406020C1 (en) 1994-02-24 1994-02-24 Automatic digital image recognition system

Country Status (1)

Country Link
DE (1) DE4406020C1 (en)

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19610066C1 (en) * 1996-03-14 1997-09-18 Siemens Nixdorf Advanced Techn A method for detecting face-related personal data and their use for the identification or verification of individuals
WO1999046737A1 (en) 1998-03-12 1999-09-16 Zentrum Für Neuroinformatik Gmbh Method for verifying the authenticity of an image recorded during a personal identification process
WO1999053427A1 (en) * 1998-04-13 1999-10-21 Eyematic Interfaces, Inc. Face recognition from video images
WO2000010119A1 (en) * 1998-08-14 2000-02-24 Christian Eckes Method for recognizing objects in digitized images
US6192150B1 (en) 1998-11-16 2001-02-20 National University Of Singapore Invariant texture matching method for image retrieval
US6222939B1 (en) 1996-06-25 2001-04-24 Eyematic Interfaces, Inc. Labeled bunch graphs for image analysis
DE19726226C2 (en) * 1997-06-22 2001-07-26 Zentrum Fuer Neuroinformatik G A method for automated identification of structures in sections through biological cells or biological tissue
US6272231B1 (en) 1998-11-06 2001-08-07 Eyematic Interfaces, Inc. Wavelet-based facial motion capture for avatar animation
US6834115B2 (en) 2001-08-13 2004-12-21 Nevengineering, Inc. Method for optimizing off-line facial feature tracking
WO2005010803A2 (en) * 2003-07-22 2005-02-03 Cognex Corporation Methods for finding and characterizing a deformed pattern in an image
US6853379B2 (en) 2001-08-13 2005-02-08 Vidiator Enterprises Inc. Method for mapping facial animation values to head mesh positions
US6876364B2 (en) 2001-08-13 2005-04-05 Vidiator Enterprises Inc. Method for mapping facial animation values to head mesh positions
DE10361838B3 (en) * 2003-12-30 2005-05-25 RUHR-UNIVERSITäT BOCHUM Assessing real object similarities involves generating supporting point vector sets with parameterized object data, transforming according to base functions to determine complex coefficients, grouping into characteristic vector components
US6917703B1 (en) 2001-02-28 2005-07-12 Nevengineering, Inc. Method and apparatus for image analysis of a gabor-wavelet transformed image using a neural network
AU2004212509B2 (en) * 1998-04-13 2005-09-08 Google Llc Face recognition from video images
EP1580684A1 (en) * 1998-04-13 2005-09-28 Nevenengineering, Inc. Face recognition from video images
US7050624B2 (en) 1998-12-04 2006-05-23 Nevengineering, Inc. System and method for feature location and tracking in multiple dimensions including depth
US7050655B2 (en) 1998-11-06 2006-05-23 Nevengineering, Inc. Method for generating an animated three-dimensional video head
US8081820B2 (en) 2003-07-22 2011-12-20 Cognex Technology And Investment Corporation Method for partitioning a pattern into optimized sub-patterns
US8103085B1 (en) 2007-09-25 2012-01-24 Cognex Corporation System and method for detecting flaws in objects using machine vision
US8229222B1 (en) 1998-07-13 2012-07-24 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8437502B1 (en) 2004-09-25 2013-05-07 Cognex Technology And Investment Corporation General pose refinement and tracking tool
US9659236B2 (en) 2013-06-28 2017-05-23 Cognex Corporation Semi-supervised method for training multiple pattern recognition and registration tool models

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1993015475A1 (en) * 1992-01-29 1993-08-05 British Telecommunications Public Limited Company Method of forming a template

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1993015475A1 (en) * 1992-01-29 1993-08-05 British Telecommunications Public Limited Company Method of forming a template

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
BICHSEL, M., SEITZ, P.: DAGM, Springer-Verlag 1990, S. 106-113 *
BUHMANN et al.: IJCNN II, San Diego 1990, pp. 411-416 *
LODES et al.: IEEE Transaction on Computers, 42, 1993, pp. 300-311 *

Cited By (45)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19610066C1 (en) * 1996-03-14 1997-09-18 Siemens Nixdorf Advanced Techn A method for detecting face-related personal data and their use for the identification or verification of individuals
US6222939B1 (en) 1996-06-25 2001-04-24 Eyematic Interfaces, Inc. Labeled bunch graphs for image analysis
DE19726226C2 (en) * 1997-06-22 2001-07-26 Zentrum Fuer Neuroinformatik G A method for automated identification of structures in sections through biological cells or biological tissue
WO1999046737A1 (en) 1998-03-12 1999-09-16 Zentrum Für Neuroinformatik Gmbh Method for verifying the authenticity of an image recorded during a personal identification process
DE19810792A1 (en) * 1998-03-12 1999-09-16 Zentrum Fuer Neuroinformatik G Personal identity verification method for access control e.g. for automatic banking machine
US6301370B1 (en) 1998-04-13 2001-10-09 Eyematic Interfaces, Inc. Face recognition from video images
WO1999053427A1 (en) * 1998-04-13 1999-10-21 Eyematic Interfaces, Inc. Face recognition from video images
EP1580684A1 (en) * 1998-04-13 2005-09-28 Nevenengineering, Inc. Face recognition from video images
AU2004212509B2 (en) * 1998-04-13 2005-09-08 Google Llc Face recognition from video images
US8335380B1 (en) 1998-07-13 2012-12-18 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8363956B1 (en) 1998-07-13 2013-01-29 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8363972B1 (en) 1998-07-13 2013-01-29 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8331673B1 (en) 1998-07-13 2012-12-11 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8295613B1 (en) 1998-07-13 2012-10-23 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8270748B1 (en) 1998-07-13 2012-09-18 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8265395B1 (en) 1998-07-13 2012-09-11 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8254695B1 (en) 1998-07-13 2012-08-28 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8229222B1 (en) 1998-07-13 2012-07-24 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8249362B1 (en) 1998-07-13 2012-08-21 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8363942B1 (en) 1998-07-13 2013-01-29 Cognex Technology And Investment Corporation Method for fast, robust, multi-dimensional pattern recognition
US8867847B2 (en) 1998-07-13 2014-10-21 Cognex Technology And Investment Corporation Method for fast, robust, multi-dimensional pattern recognition
US8244041B1 (en) 1998-07-13 2012-08-14 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US8320675B1 (en) 1998-07-13 2012-11-27 Cognex Corporation Method for fast, robust, multi-dimensional pattern recognition
US7113641B1 (en) 1998-08-14 2006-09-26 Christian Eckes Method for recognizing objects in digitized images
WO2000010119A1 (en) * 1998-08-14 2000-02-24 Christian Eckes Method for recognizing objects in digitized images
DE19837004C1 (en) * 1998-08-14 2000-03-09 Christian Eckes Method for recognizing objects in digitized images
US6272231B1 (en) 1998-11-06 2001-08-07 Eyematic Interfaces, Inc. Wavelet-based facial motion capture for avatar animation
US7050655B2 (en) 1998-11-06 2006-05-23 Nevengineering, Inc. Method for generating an animated three-dimensional video head
US6192150B1 (en) 1998-11-16 2001-02-20 National University Of Singapore Invariant texture matching method for image retrieval
US7050624B2 (en) 1998-12-04 2006-05-23 Nevengineering, Inc. System and method for feature location and tracking in multiple dimensions including depth
US6917703B1 (en) 2001-02-28 2005-07-12 Nevengineering, Inc. Method and apparatus for image analysis of a gabor-wavelet transformed image using a neural network
US6853379B2 (en) 2001-08-13 2005-02-08 Vidiator Enterprises Inc. Method for mapping facial animation values to head mesh positions
US6876364B2 (en) 2001-08-13 2005-04-05 Vidiator Enterprises Inc. Method for mapping facial animation values to head mesh positions
US6834115B2 (en) 2001-08-13 2004-12-21 Nevengineering, Inc. Method for optimizing off-line facial feature tracking
WO2005010803A2 (en) * 2003-07-22 2005-02-03 Cognex Corporation Methods for finding and characterizing a deformed pattern in an image
WO2005010803A3 (en) * 2003-07-22 2005-06-23 Cognex Corp Methods for finding and characterizing a deformed pattern in an image
US8081820B2 (en) 2003-07-22 2011-12-20 Cognex Technology And Investment Corporation Method for partitioning a pattern into optimized sub-patterns
US7190834B2 (en) 2003-07-22 2007-03-13 Cognex Technology And Investment Corporation Methods for finding and characterizing a deformed pattern in an image
US9147252B2 (en) 2003-07-22 2015-09-29 Cognex Technology And Investment Llc Method for partitioning a pattern into optimized sub-patterns
DE10361838B3 (en) * 2003-12-30 2005-05-25 RUHR-UNIVERSITäT BOCHUM Assessing real object similarities involves generating supporting point vector sets with parameterized object data, transforming according to base functions to determine complex coefficients, grouping into characteristic vector components
WO2005064526A1 (en) * 2003-12-30 2005-07-14 RUHR-UNIVERSITäT BOCHUM Method for the assessment of similarities of real objects
US8437502B1 (en) 2004-09-25 2013-05-07 Cognex Technology And Investment Corporation General pose refinement and tracking tool
US8103085B1 (en) 2007-09-25 2012-01-24 Cognex Corporation System and method for detecting flaws in objects using machine vision
US9659236B2 (en) 2013-06-28 2017-05-23 Cognex Corporation Semi-supervised method for training multiple pattern recognition and registration tool models
US9679224B2 (en) 2013-06-28 2017-06-13 Cognex Corporation Semi-supervised method for training multiple pattern recognition and registration tool models

Similar Documents

Publication Publication Date Title
Craw et al. Finding face features
Zunino et al. Vector quantization for license-plate location and image coding
Beymer Face recognition under varying pose
Chua et al. 3D human face recognition using point signature
Zou et al. A comparative study of local matching approach for face recognition
Sung et al. Example Based Learning for View-Based Human Face Detection.
US6628834B2 (en) Template matching system for images
JP4443722B2 (en) Image recognition apparatus and method
US5105467A (en) Method of fingerprint verification
US6314197B1 (en) Determining an alignment estimation between two (fingerprint) images
DE60016589T2 (en) Method and device for generating a compound finger imprint image
US6876757B2 (en) Fingerprint recognition system
US6901155B2 (en) Wavelet-enhanced automated fingerprint identification system
US6181805B1 (en) Object image detecting method and system
Ross et al. A hybrid fingerprint matcher
Zhang et al. Fingerprint classification based on extraction and analysis of singularities and pseudo ridges
US4901362A (en) Method of recognizing patterns
Kawaguchi et al. Detection of eyes from human faces by Hough transform and separability filter
EP1774470B1 (en) Face recognition method and apparatus therefor
US6185318B1 (en) System and method for matching (fingerprint) images an aligned string-based representation
Tabassi et al. Fingerprint image quality
CA2571643C (en) Single image based multi-biometric system and method
US6356649B2 (en) “Systems and methods with identity verification by streamlined comparison and interpretation of fingerprints and the like”
US7142699B2 (en) Fingerprint matching using ridge feature maps
de Martin-Roche et al. Iris recognition for biometric identification using dyadic wavelet transform zero-crossing

Legal Events

Date Code Title Description
D1 Grant (no unexamined application published) patent law 81
8100 Publication of the examined application without publication of unexamined application
8364 No opposition during term of opposition
8327 Change in the person/name/address of the patent owner

Owner name: ZN VISION TECHNOLOGIES AG, 44801 BOCHUM, DE

8327 Change in the person/name/address of the patent owner

Owner name: VIISAGE TECHNOLOGY AG, 44801 BOCHUM, DE

8339 Ceased/non-payment of the annual fee