Abstract:
A system and method for dating gelatin silver photographic paper is provided. The system and method includes providing a database management system having temporal physical characteristic profiles. Each temporal physical characteristic profile consists of a pulp composition percentage characteristic; a fiber composition characteristic; a thickness characteristic; and an optical brightening agent composition. The system implements a program of instructions to determine a, probable date range for each temporal physical characteristic profile and also determines a physical characteristic profile associated with the gelatin silver photograph. Dating the photograph is accomplished by the system determining the closest match between the temporal physical characteristic profile and the physical characteristic profile associated with the gelatin silver photograph.

Description:
BACKGROUND  
       [0001]    1. Field of Use 
         [0002]    These teachings relate generally to forensic photograph dating and more particularly to systems employing digital computers for determining the probabilistic date of a physical characteristic associated with a photograph. 
         [0003]    2. Description of Prior Art (Background) 
         [0004]    A photographic print&#39;s date is elementary to the understanding of the work, its historical context and the photographer&#39;s artistic intent. It carries implications for its treatment, display and storage and can manifestly influence its market value. Recently, photographs have become the target of forgers, and as the market value of these works increase, so will forgery continue. The detection of forged photography is particularly difficult, as experts must be able to tell the difference between originals and reprints. In addition, a forger&#39;s possession of the photo-negatives would allow a forger to print an unlimited number of fake prints, which then can be passed off as original. Since the composition of photographic paper was frequently changed, fake photographs are not likely to be printed on modern photographic paper or photographic paper not contemporaneous with the original photograph. Therefore, there is a need for a system to non-destructively date photographs. 
       BRIEF SUMMARY  
       [0005]    The foregoing and other problems are overcome, and other advantages are realized, in accordance with the presently preferred embodiments of these teachings. When a gelatin silver print, typically comprised of a paper support layer, a baryta layer, an image layer and often a coating layer, is subject to investigation, tangible information, such as the presence of optical brightening agents ( 1 ), manufacturer back printing ( 2 ), paper fiber analysis ( 3 ), and surface texture characterization ( 4 ), can corroborate dates range. Thus, in accordance with one embodiment of the present invention, a processor-based method for dating a gelatin silver print having a paper base layer, an optional baryta layer, a gelatin binder layer, and an optional protective gelatin layer is provided. The processor-based method includes providing a temporal classification dataset having temporal physical characteristics associated with a predetermined date. The processor-based method also includes determining at least one physical characteristic associated with the gelatin silver print and determining a manufacturing date associated with the gelatin silver print by determining the closest probability match between the temporal physical characteristics and the at least one physical characteristic associated with the gelatin silver print. 
         [0006]    In accordance with another embodiment the invention includes a system for dating a gelatin silver print having a paper base layer, a baryta layer (often but not always required), a gelatin binder layer, and a protective gelatin layer. The system includes at least one temporal pulp composition characteristic associated with a predetermined date dataset. The system also includes a program storage device for containing a program of instructions executable by the machine to determine at least one physical characteristic associated with the gelatin silver print and determining the closest probability match between the at least one temporal pulp composition characteristic associated with the predetermined date and the at least one physical characteristic associated with the gelatin silver print. 
         [0007]    The invention is also directed towards a method for dating gelatin silver photographs. The method includes providing a temporal physical characteristic dataset having temporal physical characteristic profiles. Each temporal physical characteristic profile consists of a pulp composition percentage characteristic; a fiber composition characteristic; a thickness characteristic; and an optical brightening agent composition, The optical brightening agent composition includes an optical brightening agent composition associated with the photographic paper layer and a second optical brightening agent associated with the photographic paper emulsion layer. Also included in the characteristic profile is a Barium/Strontium ratio characteristic. The method includes determining a probable date range for each temporal physical characteristic profile and determining a physical characteristic profile associated with the gelatin silver photograph. Dating the photograph is accomplished by determining the closest match between the temporal physical characteristic profile and the physical characteristic profile associated with the gelatin silver photograph. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
           [0009]      FIG. 1  is a block diagram of a system configuration of an embodiment of the present invention; 
           [0010]      FIG. 2A  and  FIG. 2B  are block diagrams of an example classification regression tree for dating gelatin silver photographs in accordance with the invention shown in  FIG. 1 ; 
           [0011]      FIG. 3  is a pictorial example of a rag pulp method dataset in accordance with the invention shown in  FIG. 1 ; 
           [0012]      FIG. 4  is a pictorial example of clustering the rag pulp method dataset shown in  FIG. 3 ; 
           [0013]      FIG. 5  is a pictorial example of multiple pulp method datasets in accordance with the invention shown in  FIG. 1 ; and 
           [0014]      FIG. 6A  and  FIG. 6B  are method flow charts for determining the data range of a gelatin silver photograph in accordance with the invention shown in  FIG. 1 . 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    With reference now to  FIG. 1 , a block diagram illustrating a system  300  for dating gelatin silver photographs is depicted in which the present invention may be implemented. System  300  employs a peripheral component interconnect (PCI) local bus architecture. Although the depicted example employs a PCI bus, other bus architectures such as Accelerated Graphics Port (AGP) and Industry Standard Architecture (ISA) may be used. Processor  302  and main memory  304  are connected to PCI local bus  306  through PCI bridge  308 . PCI bridge  308  also may include an integrated memory controller and cache memory for processor  302 . Additional connections to PCI local bus  306  may be made through direct component interconnection or through add-in boards. 
         [0016]    In the depicted example, local area network (LAN) adapter  310 , SCSI host bus adapter  312 , and expansion bus interface  314  are connected to PCI local bus  306  by direct component connection. It will be understood that LAN adapter  310  may also include an internet browser. In contrast, audio adapter  316 , graphics adapter  318 , and audio/video adapter  319  are connected to PCI local bus  306  by add-in boards inserted into expansion slots. Expansion bus interface  314  provides a connection for a keyboard and mouse adapter  320 , modem  322 , and additional memory  324 . Small computer system interface (SCSI) host bus adapter  312  provides a connection for hard disk drive  326 , tape drive  328 , and CD-ROM drive  330 . Typical PCI local bus implementations will support PCI expansion slots or add-in connectors. 
         [0017]    An operating system runs on processor  302  and is used to coordinate and provide control of various components within data processing system  31 . Data processing system  31  may be configured to process dataset  14  as described herein. The operating system may be any suitable commercially available operating system. In addition, an object oriented programming system such as Java may run in conjunction with the operating system and provide calls to the operating system from Java programs or applications executing on data processing system  300 . “Java” is a trademark of Sun Microsystems, Inc. Instructions for the operating system, the object-oriented operating system, and applications or programs are located on storage devices, such as hard disk drive  326 , and may be loaded into main memory  304  for execution by processor  302 . 
         [0018]    System  300  may be configured to regressively cluster dataset  14  to allocate data points within the dataset to a probable date range. In some embodiments, such an adaptation may be incorporated within system  300 . In particular, system  300  may include storage medium  324  with program instructions  13  executable by processor  16  to regressively cluster dataset  14 . In an embodiment in which dataset  14  is external to system  10 , however, the adaptation to regressively cluster dataset  14  may be additionally, or alternatively, incorporated within the respective data source/s of dataset  14 . In particular, the data source/s of dataset  14 , in such an embodiment, may include a storage medium with program instructions which are executable through a processor for regressively clustering data. 
         [0019]    In general, input may be transmitted to system  300  to execute program instructions  13  within storage medium  324 . Storage medium  324  may include any device for storing program instructions, such as a read-only memory, a random access memory, a magnetic or optical disk, or a magnetic tape. Program instructions  13  may include any instructions by which to perform the method or regression clustering and classification processes described below. In particular, program instructions  13  may include instructions for correlating variable parameters of a dataset and other instructions for clustering the dataset through the iteration of a regression algorithm. In this manner, program instructions  13  may used to generate a plurality of different functions correlating variable parameters of a dataset. In addition, program instructions  13  may include instructions for determining directives by which to classify new data into the dataset with respect to the generated functions. In some cases, program instructions  13  may further include instructions by which to receive new data and predict values of variable parameters associated with the new data and dataset. 
         [0020]    Those of ordinary skill in the art will appreciate that the hardware in  FIG. 1  may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash read-only memory (ROM), equivalent nonvolatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in  FIG. 1 . 
         [0021]    The depicted example in  FIG. 1  and above-described examples are not meant to imply architectural limitations. For example, system  300  also may be a notebook computer or hand held computer in addition to taking the form of a PDA. 
         [0022]    Before describing the new system in accordance with the invention, it would be helpful to describe the classification/regression tree methodology and an illustrative classification/regression tree. The general classification/regression tree methodology is described in detail in L. Breiman, Classification and Regression Trees, (Wadsworth &amp; Brooks/Cole Advanced Books &amp; Software, Pacific Grove, Calif.: 1984). The methodology provides an arrangement for classifying individual records in, for example, a database, to a selected class based on data contained in the respective records. For each record, the methodology makes use of a set of inquiries applied to data contained in each record. First an initial inquiry is applied, and the response to that inquiry provides a basis for selecting a subsequent inquiry. This process is repeated through a series of inquiries until a response to a particular inquiry identifies a class to which the record is to be assigned. Generally, the series of inquiries applied to a particular record is a subset of the entire set of inquiries that may be applied. If each inquiry is analogized to a node, the set of nodes and their order of application resemble a tree. Such a tree can be used to perform classification and/or regression analysis, and will be generally identified herein as a “classification/regression tree.” 
         [0023]    More specifically, and with reference to  FIG. 2A  and  FIG. 2B , a pulp method classification/regression tree  10  is a binary tree that includes a number of nodes extending from a root node  101  to a number of leaf nodes  102 - 113 . The tree  10  is used to classify a record into one of a selected number of classes. A record includes a number of fields, one of which is identified as a “dependent” field or dependent variable, with the remaining fields being identified as “independent” fields or independent variables. The diverse values which the data contained in the dependent field can take on identify the “classes” into which the records can be classified, and the classification of each record is based on the values contained in the independent fields; otherwise stated, the tree  10  determines a value for the dependent field (the record&#39;s class), which is otherwise unknown, based on the contents of some or all of the independent fields. 
         [0024]    Each node  101 - 113  in the tree  10  represents a query to be applied to one of the independent fields, with the response to the query comprising a “yes” or a “no.” An illustrative query may have the form “Is the value of “cotton percentage” (equal to or less than,) a selected value  10 A 1 ?”, where field “cotton percentage” is a field containing one of the independent variables and the selected value is a value determined by the tree generating system while generating the tree. If the answer to the query at a particular node is a “yes,” the query at its left child node is applied, but if the answer is a “no,” the query at the node&#39;s right child node is applied, and so the “selected value” used in each query, which is determined for the corresponding node during tree generation will be referred to herein as a “splits” value. 
         [0025]    The queries of the tree are applied beginning with the root node  101 ; if the response of the root node&#39;s query is “yes,” the left child node&#39;s question is then applied to the record; on the other hand, if the answer to the root node&#39;s query is “no,” the right child node&#39;s query is then applied to the record. This process is repeated for the record at each node from the root node along a path through a series of intermediate nodes to the last, or terminal, node, and the response at the leaf node identifies the class into which the record should be classified; that is, the response at the terminal node provides an indication as to the value of the dependent field. The particular class identified by the classification/regression tree  10  for a particular record has an associated probability or confidence value indicating the likelihood that the record is, in fact, properly classified in the class identified by the tree  10 . 
         [0026]    For example, still referring to  FIG. 2A  and  FIG. 2B , and for example purposes only, Node- 0   102 , and also  FIG. 3 , it will be appreciated that the majority of rag pulp (derived from cotton or flax) data points in temporal rag pulp dataset  201  for years less than  1940  (i.e., those data points to the left of line  202 ) can be calculated to be above 1.5%. It will be appreciated that data points within a database may be statistically grouped by any suitable means. For example, data points in section  204 , i.e., those data points calculated to be above 1.5% may be statistically grouped as a standard or normal distribution group with a mean and standard deviation as shown in Node  1   102 . 
         [0027]    Referring also to  FIG. 4 , it will be appreciated that rag or cotton pulp data points may be clustered as shown by cluster circles  302  and  303 . Center points, or comparison means, for classifying new data points within the dataset, may be calculated by suitable processes, such as, for example, K-means clustering 
         [0028]    As is known in the art it will be appreciated that the partition of the dataset  14  can be “hard” or “soft.” A “hard” partition may refer to the designation of every of data point within dataset  14  belonging to a specific subset of data points. In this manner, the partitions of the data points may be clear and distinct. A “soft” partition, however, may refer to the ambiguous groupings of data points within subsets of dataset  14 . In some cases, such a categorization of data points may depend on the probability of data points belonging to particular subsets within dataset  14  rather than other subsets. 
         [0029]    Referring now to  FIG. 6A  and  FIG. 6B , there are shown flowcharts illustrating one method for determining a probable date range for a gelatin silver photograph. Process  61  processes a physical characteristic dimension dataset, such as, for example, the dataset shown in  FIG. 5 . The dataset may be processed by any suitable means such that the dataset represents a likely date range for each physical characteristic in the dataset, or a group of physical characteristics in the dataset. For example, a physical characteristic profile  501  shown in  FIG. 5  may be used to compare with a physical characteristic profile determined for a photograph of interest. 
         [0030]    Still referring to  FIG. 6A  and  FIG. 6B , process  62  determines a physical characteristic dimension associated with the gelatin silver photograph. The physical characteristic may be any suitable physical pulp characteristic such as, for example, grass, hardwood bleached kraft, hardwood soda/kraft, hardwood, hardwood bleached kraft/soda, rag or cotton, hardwood bleached alpha, hardwood bleached sulfite, softwood bleached alpha. In addition, the physical characteristic may also be any suitable chemical composition or ratio, such as, for example, Barium/Strontium ratio or an X-ray fluorescence characteristic. The dimension associated with the physical characteristic may be any suitable dimension such as, for example, weight, or thickness. 
         [0031]    It will also be appreciated that the dimension may be stated in terms of a percentage or ratio of the physical characteristic to other physical characteristics associated with the gelatin silver photograph. It will also be understood that a physical dimension may also include location of a physical characteristic, such as, for example, the presence or absence of an optical brightening agent on the front or back of the photograph. Combiner  600  combines information from process  62  and dataset  61 . Decision point  64  determines if the physical characteristic dimension exceeds a predetermined threshold. For example, see  FIG. 2B ,  10 E 2  where SBkraft (Softwood bleached kraft) exceeds 0.5%. 
         [0032]    Continuing with  FIG. 6 , process  66  determines a probable date range associated with the physical characteristic dimension below a predetermined threshold. For example, referring to  FIG. 2B , Node- 12   113 , and the probable, or mean, date range for SBkraft (Softwood bleached kraft) exceeding 0.5% is approximately  1960  with a standard deviation of approximately 4 years. Process  68  continues the analysis with determining a second physical characteristic associated with the gelatin silver print. Decision point  601  determines if the second physical characteristic exceeds the predetermined threshold for that characteristic. Process  603  determines another probable date range associated with the second physical characteristic dimension that is at least partially concurrent with the preceding date range. It will be understood that the predicted date for a particular physical characteristic has a higher probability of being within one standard deviation from the mean date. It will be further understood that the chained probabilities, i.e., for example, referring to  FIG. 2A  and  FIG. 2B , Node- 1   102 , Node- 3   104 , Node  7   108  may be suitably combined to generate the most likely comprehensive date range for the physical characteristic dimensions within the chain. 
         [0033]    It should be understood that the foregoing description is only illustrative of the invention. Thus, various alternatives and modifications can be devised by those skilled in the art without departing from the invention. Accordingly, the present invention is intended to embrace all such alternatives, modifications and variances that fall within the scope of the appended claims.