Abstract:
The subject matter of this specification can be embodied in, among other things, a computer-implemented method that includes receiving a plurality of images having human faces. The method further includes generating a data structure having representations of the faces and associations that link the representations based on similarities in appearance between the faces. The method further includes outputting a first gender value for a first representation of a first face that indicates a gender of the first face based on one or more other gender values of one or more other representations of one or more other faces that are linked to the first representation.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation application of and claims priority to U.S. application Ser. No. 13/274,778, filed on Oct. 17, 2011, which claims priority to U.S. application Ser. No. 11/934,547, filed on Nov. 2, 2007, the entire contents of which are hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     This instant specification relates to inferring the gender of a face in an image. 
     BACKGROUND 
     In many application domains, such as photo collection, social networking, surveillance, adult-content detection, etc, it is desirable to use an automated method to determine the gender of a person in an image. Some systems use computer vision algorithms in an attempt to determine gender by directly analyzing a face within an image to determine if the face is associated with typically male or female characteristics. 
     SUMMARY 
     In general, this document describes inferring the gender of a person or animal from a set of images. The faces are represented in a data structure and includes links between faces having similarities. 
     In a first aspect, a computer-implemented method includes receiving a plurality of images having human faces. The method further includes generating a data structure having representations of the faces and associations that link the representations based on similarities in appearance between the faces. The method further includes outputting a first gender value for a first representation of a first face that indicates a gender of the first face based on one or more other gender values of one or more other representations of one or more other faces that are linked to the first representation. 
     In a second aspect, a computer-implemented method includes receiving a plurality of images having animal faces. The method further includes generating a graph having nodes representing the faces and edges that link the nodes based on similarities in appearance between the faces represented by the nodes. The method further includes outputting a first gender value for a first node that indicates a gender of a first face associated with the first node based on one or more second gender values of one or more neighboring nodes in the graph. 
     In a third aspect, a computer-implemented method includes receiving a plurality of images having animal faces. The method further includes generating a data structure that associates a first face with one or more second faces based on similarities in appearance between the first face and the one or more second faces. The method further includes outputting a first gender value for the first face based on one or more gender values previously associated with the one or more second faces. 
     In a fourth aspect, a system includes a face detector for identifying faces within images. The system further includes means for generating a data structure configured to associate a first face with one or more second faces based on similarities in appearance between the first face and the one or more second faces. The system further includes an interface to output a first gender value for the first face based on one or more gender values previously associated with the one or more second faces. 
     The systems and techniques described here may provide one or more of the following advantages. First, an automated method is provided for identifying the gender of a person (or persons) in a collection of images. Second, a gender detection method is provided that does not require large data sets for training a statistical gender classifier. Third, a method for gender detection is provided that does not rely on computer vision techniques to directly identify a gender of a face based on an image of the face. Fourth, genders associated with androgynous faces within images can be more accurately determined. 
     The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic diagram showing an example of a system for inferring the gender of a face in an image. 
         FIG. 2  is a block diagram showing another example of a system for inferring the gender of a face in an image. 
         FIG. 3A  shows examples of images used for inferring a gender of a face in an image. 
         FIG. 3B  shows examples of comparisons and similarities between faces in images. 
         FIG. 3C  shows an example of a weighted undirected graph of the similarities between the faces in the images. 
         FIG. 3D  shows an example of manually identified gender values for a portion of the weighted undirected graph. 
         FIG. 4  is a flow chart showing an example of a method for constructing a weighted undirected graph of similarities between faces in images. 
         FIG. 5  is a flow chart showing an example of a method for inferring the gender of a face in an image using a weighted undirected graph of similarities between faces in images. 
         FIGS. 6A-D  show examples of gender values in a weighted undirected graph before performing a gender inferring process and after one iteration, two iterations, and four iterations of the gender inferring process, respectively. 
         FIG. 7A  shows an example of a table that stores gender values for faces. 
         FIG. 7B  shows an example of a table that stores similarity scores between a first face and other faces. 
         FIG. 8  shows an example of a weighted undirected graph including dummy labels. 
         FIG. 9  is a schematic diagram showing an example of a generic computing system that can be used in connection with computer-implemented methods described in this document. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     This document describes systems and techniques for inferring the gender of a face in an image. A network, such as the Internet or a wide area network, can include servers hosting images of people and/or other animals. In general, an image of a female face may have similarities with images of other female faces and an image of a male face may have similarities with images of other male faces. In certain implementations, a gender determination system analyzes the similarities between the faces, including faces having a known gender and faces having an unknown gender. The gender determination system can use the similarities and the known genders to infer a gender of one or more faces having an unknown gender. 
     In certain implementations, a face having an unknown gender may have little or no direct similarities with the faces having a known gender. The gender determination system may determine that the face having the unknown gender has similarities with one or more other unknown gender faces. The gender determination system may also determine that one or more of the other unknown gender faces have similarities with one or more faces having known genders. The gender determination system can use the chain of intermediate faces to infer the gender of the face having an unknown gender by indirectly relating the unknown gender face to a face having a known gender. 
       FIG. 1  is a schematic diagram showing an example of a system  100  for inferring the gender of a face in an image. The system  100  includes a gender determination system  102  and multiple publisher systems  104   a - c , such as a web logging (blogging) server, an image hosting server, and a company web server, respectively. The gender determination system  102  communicates with the publisher systems  104   a - c  using one or more networks, such as the Internet, and one or more network protocols, such as Hypertext Transport Protocol (HTTP). 
     In the case of HTTP communication, the publisher systems  104   a - c  can include multiple web pages  106   a - c , respectively. For example, the web page  106   a  published by the blogging system is a celebrity gossip blog, the web page  106   b  published by the image hosting system is an image gallery, and the web page  106   c  published by the web server system is a homepage for the ACME Corporation. The web pages  106   a - c  each include one or more images  108   a - e . The images  108   a - e  include faces of people and/or other animals. The gender determination system  102  retrieves the images  108   a - e  from the publisher systems  104   a - c  and stores the images  108   a - e  in a data storage  110 . 
     The gender determination system  102  includes an inferred gender label generator  112  that performs processing to infer the gender of the faces in the images. The inferred gender label generator  112  includes a face detector  114  and a similarity calculator  116 . The face detector  114  uses face detection algorithms known to those skilled in the art to detect faces in the images  108   a - e . In certain implementations, the face detector  114  stores the portions of the images  108   a - e  that include faces in the data storage  110 . Alternatively, the face detector  114  may store information that describes the location of the face portions within the images  108   a - e . The face detector  114  passes the face information to the similarity calculator  116  or otherwise notifies the similarity calculator  116  of the face information. 
     The similarity calculator  116  calculates similarity scores between pairs of faces. The similarity calculator  116  uses facial analysis algorithms known to those skilled in the art. For example, one such facial analysis algorithm is discussed in a paper entitled “Transformation, Ranking, and Clustering for Face Recognition Algorithm Comparison,” by Stefan Leigh, Jonathan Phillips, Patrick Grother, Alan Heckert, Andrew Ruhkin, Elaine Newton, Mariama Moody, Kimball Kniskern, Susan Heath, published in association with the Third Workshop on Automatic Identification Advanced Technologies, Tarrytown, March 2002. 
     The inferred gender label generator  112  includes or has access to predetermined gender information for one or more of the faces. For example, a user may make an input indicating that a face in the image  108   a  has a gender of female and a face in the image  108   b  has a gender of male. Alternatively, the inferred gender label generator  112  may generate predetermined gender information based on an algorithm, such as associating a distinguishing feature within a face as male or female. The inferred gender label generator  112  uses the predetermined gender information and the similarity scores between the faces to link each of the faces directly or indirectly to the faces having predetermined or known genders. The inferred gender label generator  112  uses the links to infer a gender for the faces. The inferred gender label generator  112  stores the similarity scores and inferred genders in the data storage  110  as a gender label value table  118 . 
     The gender determination system  102  can output genders of the faces as gender label information  120   a - e  for the images  108   a - e , respectively, to the publisher systems  104   a - c . In another implementation, the gender determination system  102  can include, or provide gender information to, an image search engine. For example, a user may make an input indicating a search for images of people or animals having a particular gender. The gender determination system  102 , or another image search system, uses the gender label information  120   a - e  to provide images having the requested gender. 
       FIG. 2  is a block diagram showing an example of a system  200  for inferring the gender of a face in an image. The system  200  includes the gender determination system  102  as previously described. The inferred gender label generator  112  receives the images  108   a - e  and may store the images  108   a - e  in the data storage  110 . The inferred gender label generator  112  uses the face detector  114  to locate faces within the images  108   a - e . Subsequently or concurrently, the inferred gender label generator  112  uses the similarity calculator  116  to calculate similarity scores between faces. 
     In the system  200 , the similarity calculator  116  is included in a graph generator  202 . The graph generator  202  generates a graph of the faces in the images  108   a - e . Each face represents a node in the graph. The graph generator  202  represents the similarity scores between faces as edges that link the nodes. Each edge is associated with its similarity score. The edges linking the nodes may be undirected, bi-directional, or uni-directional. For the purposes of clarity, the edges in the following examples are undirected unless otherwise specified. 
     The inferred gender label generator  112  associates each face with one or more gender labels and each gender label may have a gender label value. In some implementations, the gender label values are numeric values, such as decimal numbers. For example, the face in the image  108   a  may have gender labels of “Male” and “Female” with associated gender label values of “0.00” and “1.00,” respectively. The inferred gender label generator  112  stores the gender label values in the gender label value table  118 . 
     In some implementations, the gender determination system  102  includes a data storage  204  that stores one or more predetermined gender label values  206 . The gender determination system  102  may receive the predetermined gender label values, for example, from a user input. The graph generator  202  associates the predetermined gender label values  206  with corresponding nodes in the graph. For example, a user may input the gender label values of “0.00” Male and “1.00” Female for the face in the image  108   a . The graph generator  202  associates the gender label values with the node in the graph that represents the face from the image  108   a . The inferred gender label generator  112  subsequently uses the predetermined gender label values  206  as a starting point, or seed, for the algorithm to infer other gender label values of faces included in other images. 
       FIG. 3A  shows examples of the images  108   a - e  used for inferring a gender of a face in an image. The gender determination system  102  may use images such as the images  108   a - e  or other images. The gender determination system  102  may also use fewer or more images than are shown here. The images  108   a - e  all include a person having a face. In some implementations, an image may include multiple people or animals. In this example, the image  108   a  is an adult female, the image  108   b  is an adult male, the image  108   c  is a female child, the image  108   d  is a male child, and the image  108   e  is a picture of a somewhat androgynous adult. 
       FIG. 3B  shows examples of comparisons and similarities between multiple faces  302   a - e  in the images  108   a - e . The face detector  114  identifies regions within the images  108   a - e  that represent the faces  302   a - e . The similarity calculator  116  compares features between faces to determine similarity scores between faces. In some implementations, the similarity calculator  116  compares pairs of faces and determines a similarity score for each pair of faces. In some implementations, the similarity score is a decimal number in the range from zero to one, where zero indicates no similarity and one indicates that the faces are identical or indistinguishable. 
     For example, the similarity calculator  116  may determine similarity scores of “0.7” between the faces  302   b  and  302   d, “ 0.5” between the faces  302   b  and  302   e , and “0.7” between the faces  302   a  and  302   c . The similarity scores of “0.7” indicate that the faces  302   b  and  302   d  are significantly similar and the faces  302   a  and  302   c  are significantly similar. The similarity score of “0.5” indicates that the faces  302   b  and  302   e  are only somewhat similar. 
     The similarity calculator  116  may perform the comparison and calculate a similarity score for each pair of images. The graph generator  202  uses the similarity scores to generate the graph representing similarities between the faces  302   a - e.    
       FIG. 3C  shows an example of a weighted undirected graph  310  of the similarities between the faces  302   a - e  in the images  108   a - e . The graph generator  202  represents the faces  302   a - e  with multiple nodes  312   a - e , respectively. The graph generator  202  links pairs of nodes with edges that represent a similarity score between the linked pair of nodes. For example, the graph generator  202  links the nodes  312   b  and  312   d  with an edge having an associated similarity score of “0.7.” In some implementations, the graph generator  202  generates an edge between each pair of nodes in the weighted undirected graph  310 . In some implementations, the graph generator  202  links a portion of the pairs of nodes with edges. For example, the graph generator  202  can link pairs of nodes having a threshold similarity score. In another example, the graph generator  202  can link a threshold number of nodes to a particular node, such as the five nodes having the highest similarity scores with the particular node. For the purposes of clarity in explanation, the weighted undirected graph  310  only includes edges between neighboring nodes. 
       FIG. 3D  shows an example of manually identified gender values for a portion of the weighted undirected graph  310 . As previously described, the gender determination system  102  includes the predetermined gender label values  206 . The graph generator  202  associates the predetermined gender label values  206  with corresponding nodes. For example, the graph generator  202  associates gender label values of “1.0” Male and “0.0” Female with the node  312   b  and “0.0” Male and “1.0” Female with the node  312   a.    
     In this example of a weighted undirected graph, each node eventually has a Male gender label and a Female gender label (e.g., either predetermined or inferred). The nodes  312   a - b  have predetermined Male and Female gender labels and label values. As described below, the inferred gender label generator  112  uses the weighted undirected graph  310  to infer Male and Female gender label values for the nodes  312   c - e . Although this example describes two gender labels, in other implementations, a node may have zero, three, or more gender labels as well. 
     In some implementations, the inferred gender label generator  112  also infers gender label values for nodes having predetermined gender label values. Inferred gender label values for nodes having predetermined gender label values can be used to check the accuracy of the inferred gender label generator  112  and/or to check for possible errors in the predetermined gender label values  206 . 
       FIG. 4  is a flow chart showing an example of a method  400  for constructing a weighted undirected graph of similarities between faces in images. In some implementations, the face detector  114 , the similarity calculator  116 , and the graph generator  202  can include instructions that are executed by a processor of the gender determination system  102 . 
     The method  400  can start with step  402 , which identifies face regions using a face detector. For example, the inferred gender label generator  112  can use the face detector  114  to locate the regions of the images  108   b  and  108   d  that include the faces  302   b  and  302   d , respectively. 
     In step  404 , the method  400  compares two faces and generates a similarity score representing the similarity between the two faces. For example, the similarity calculator  116  compares the faces  302   b  and  302   d  and calculates the similarity score of “0.7” between the faces  302   b  and  302   d.    
     If at step  406  there are more faces to compare, then the method  400  returns to step  404  and compares another pair of faces. Otherwise, the method  400  constructs a weighted undirected graph at step  408 , where the faces are nodes and the similarity scores are edges that link the nodes. For example, the graph generator  202  constructs the nodes  312   b  and  312   d  representing the faces  302   b  and  302   d . The graph generator  202  links the nodes  312   b  and  312   d  with an edge associated with the similarity score of “0.7” between the nodes  312   b  and  312   d.    
     At step  410 , the method  400  receives a user input labeling a subset of the face nodes as either male or female. For example, the gender determination system  102  may receive a user input including the predetermined gender label values  206 . The predetermined gender label values  206  indicate that the node  312   b  has gender label values of “1.0” Male and “0.0” Female. The inferred gender label generator  112  uses the weighted undirected graph  310  and the predetermined gender label values  206  to infer the gender of one or more faces represented by nodes in the weighted undirected graph  310 . 
       FIG. 5  is a flow chart showing an example of a method  500 , referred to here as an “adsorption” algorithm, for inferring the gender of a face in an image using a weighted undirected graph of similarities between faces in images. In some implementations, the inferred gender label generator  112  can include instructions that are executed by a processor of the gender determination system  102 . 
     The adsorption algorithm  500  can be executed using information from the graphs shown in  FIGS. 3C-D  and can be executed for every node in the graphs. The adsorption algorithm  500  can start with step  502 , which determines if a specified number of iterations have run for a graph. If the number is not complete, step  504  is performed. In step  504 , a node representing a face is selected. For example, the inferred gender label generator  112  can select a node that has gender label values that may be modified by the algorithm, such as “Male” or “Female.” 
     In step  506 , a gender label is selected from the selected node. For example, the inferred gender label generator  112  can select the gender label “Male” if present in the selected node. In step  508 , a gender label value for the selected gender label is initialized to zero. For example, the inferred gender label generator  112  can set the gender label value for “Male” to zero. 
     In step  510 , a neighboring node of the selected node can be selected. For example, the selected node may specify that a neighboring node has a similarity score of “0.7” with the selected node. The inferred gender label generator  112  can select the neighboring node. 
     In step  512 , a corresponding weighted gender label value of a selected neighbor is added to a gender label value of the selected gender label. For example, if the selected neighboring node has a gender label value for the gender label “Male,” the inferred gender label generator  112  can add this value to the selected node&#39;s gender label value for “Male.” In certain implementations, the gender label value retrieved from a neighboring node can be weighted to affect the contribution of the gender label value based on the degree of distance from the selected node (e.g., based on whether the neighboring node is linked directly to the selected node, linked by two edges to the selected node, etc.) In some implementations, the gender label value can also be based on a weight or similarity score associated with the edge. 
     In step  514 , it is determined whether there is another neighbor node to select. For example, the inferred gender label generator  112  can determine if the selected node is linked by a single edge to any additional neighbors that have not been selected. In another example, a user may specify how many degrees out (e.g., linked by two edges, three edges, etc.) the inferred gender label generator  112  should search for neighbors. If there is another neighbor that has not been selected, steps  510  and  512  can be repeated, as indicated by step  514 . If there is not another neighbor, step  516  can be performed. 
     In step  516 , it is determined whether there is another gender label in the selected node. For example, the selected node can have multiple gender labels, such as “Male” and “Female.” If these additional gender labels have not been selected, the inferred gender label generator  112  can select one of the previously unselected gender labels and repeat steps  506 - 514 . If all the gender labels in the node have been selected, the gender label values of the selected node can be normalized, as shown in step  518 . For example, the inferred gender label generator  112  can normalize each gender label value so that it has a value between 0 and 1, where the gender label value&#39;s magnitude is proportional to its contribution relative to all the gender label values associated with that node. 
     In step  520 , it can be determined whether there are additional nodes in the graph to select. If there are additional nodes, the method can return to step  504 . If all the nodes in the graph have been selected, the method can return to step  502  to determine whether the specified number of iterations has been performed on the graph. If so, the adsorption algorithm  500  can end. 
     In certain implementations, the adsorption algorithm  500  can include the following pseudo code: 
     Set t=0 
     For each node, n, in the similarity graph, G:
         For each gender label, I:
           Initialize the gender label: n i,t =0.0;   
               

     For t=1 . . . x iterations:
         For each node used to label other nodes, n, in the similarity graph, G:
           For each gender label, I:
               Initialize the gender label value: n i,t+1 =n i,t      
               
           For each node to be labeled, n, in the similarity graph, G:
           For each gender label, I:
               Initialize the gender label value: n i,t+1 =n t,injection ;   
               
               

     //where n t,injection  is a node having a static assigned value
         For each node, n, in the similarity graph, G:
           For each node, m, that has an edge with similarity s mn , to n:
               For each gender label:
 
 n   i,t+1   =n   i,t+1 +( s   mn   *m   i,t )
   
               Normalize the weight of the gender labels at each n, so that the sum of
               the labels at each node=1.0   
               
               

     In certain implementations, after “x” iterations, the inferred gender label generator  112  can examine one or more of the nodes of the graph and probabilistically assign a gender label to each node based on the weights of the gender labels (e.g., a label with the maximum label value can be assigned to the node). 
     In some implementations, the number of the iterations is specified in advance. In other implementations, the algorithm terminates when the gender label values for the gender labels at each node reach a steady state (e.g., a state where the difference in the label value change between iterations is smaller than a specified epsilon). 
     In another alternative method, gender label values for nodes can be inferred by executing a random walk algorithm on the graphs. More specifically, in some implementations, given a graph, G, the inferred gender label generator  112  can calculate gender label values, or label weights, for every node by starting a random walk from each node. The random walk algorithm can include reversing the direction of each edge in the graph if the edge is directed. If the edge is bi-directional or undirected, the edge can be left unchanged. 
     The inferred gender label generator  112  can select a node of interest and start a random walk from that node to linked nodes. At each node where there are multiple-out nodes (e.g., nodes with links to multiple other nodes), the inferred gender label generator  112  can randomly select an edge to follow. If the edges are weighted, the inferred gender label generator  112  can select an edge based on the edge&#39;s weight (e.g., the greatest weighted edge can be selected first). 
     If during the random walk, a node is selected that is a labeling node (e.g., used as a label for other nodes), the classification for this walk is the label associated with the labeling node. The inferred gender label generator  112  can maintain a tally of the labels associated with each classification. 
     If during the random walk, a node is selected that is not a labeling node, the inferred gender label generator  112  selects the next random path, or edge, to follow. 
     The inferred gender label generator  112  can repeat the random walk multiple times (e.g., 1000s to 100,000s of times) for each node of interest. After completion, the inferred gender label generator  112  can derive the gender label values based on the tally of labels. This process can be repeated for each node of interest. 
     Additionally, in some implementations, the inferred gender label generator  112  generates a second data structure, such as a second graph. The second graph can include nodes substantially similar to the weighted undirected graph  310 . In one example, the weighted undirected graph  310  includes Male gender label values and a second graph includes Female gender label values. 
     In some implementations, the adsorption algorithm  500  can also be performed on the second graph. The inferred gender label generator  112  can select and compare the resulting label value magnitudes for a corresponding node from both the first and second graphs. In some implementations, the inferred gender label generator  112  can combine the gender label value magnitudes from each graph through linear weighting to determine a final gender label value magnitude (e.g., the first graph&#39;s gender label value contributes 0.7 and the second graph&#39;s gender label value contributes 0.3). In other implementations, the gender label values can be weighed equally to determine the final gender label value (e.g., 0.5, 0.5), or the inferred gender label generator  112  can give one gender label value its full contribution while ignoring the gender label value from the other graph (e.g., [1.0, 0.0] or [0.0, 1.0]). 
     In other implementations, the inferred gender label generator  112  can use a cross-validation method to set the contribution weights for gender label value magnitudes from each graph. For example, the inferred gender label generator  112  can access nodes in a graph, where the nodes have gender label value magnitudes that are known. The inferred gender label generator  112  can compare the actual gender label value magnitudes with the known gender label value magnitudes. The inferred gender label generator  112  can then weight each graph based on how closely its gender label values match the known gender label values. 
     In certain implementations, the inferred gender label generator  112  can compare the gender label value magnitudes to an expected a priori distribution, instead of or in addition to examining the final gender label magnitudes. For example, if a summed value of a first gender label across all nodes is 8.0 and the summed value of a second gender label across all of the nodes is 4.0, the a priori distribution suggests that first gender label may be twice as likely to occur as the second gender label. The inferred gender label generator  112  can use this expectation to calibrate the gender label value magnitudes for each node. If in a particular node, the first gender label value is 1.5 and the second gender label value is 1.0, then the evidence for the first gender label, although higher than the second gender label, is not as high as expected because the ratio is not as high as the a priori distribution. This decreases the confidence that the difference in magnitudes is meaningful. A confidence factor can be translated back into the rankings. 
     In some implementations, if the difference in magnitudes is below a confidence threshold, the inferred gender label generator  112  can rank a gender label with a lower value above a gender label with a higher value (e.g., manipulate the lower value so that it is increased to a gender label value greater than the higher value). For example, if the first gender label&#39;s value is expected to be three times the value of the second gender label, but was only 1.1 times greater than the second gender label, the inferred gender label generator  112  can rank the second gender label above the first gender label. In some implementations, the confidence factor can be kept as a confidence measure, which can be used, for example, by machine learning algorithms to weight the resultant label value magnitudes. 
     In yet other implementations, instead of or in addition to comparing the gender label value magnitudes based on the a priori distribution of the nodes, the inferred gender label generator  112  can compare the gender label value magnitudes based on an end distribution of magnitudes across all nodes. For example, the inferred gender label generator  112  can measure how different a particular node&#39;s distribution is from an average calculated across all nodes in a graph. 
       FIGS. 6A-D  show examples of gender values in a weighted undirected graph  600  before performing a gender inferring process and after one iteration, two iterations, and four iterations of the gender inferring process, respectively. Referring to  FIG. 6A , the weighted undirected graph  600  includes predetermined and initial gender values for the nodes  312   a - e . The weighted undirected graph  600  only shows Male gender labels for the sake of clarity. Other gender labels may be included in the weighted undirected graph  600  or another graph having nodes corresponding to nodes in the weighted undirected graph  600 . 
     The weighted undirected graph  600  includes predetermined gender label values for the nodes  312   a - b  of “0.00” Male and “1.00” Male, respectively. The nodes  312   c - e  have initial gender label values of “0.50” Male. In some implementations, an initial gender label value may be a predetermined value, such as “0.50.” Alternatively, the initial gender label value may be based on the average of the predetermined gender label values  206  or another set of gender label values. 
     Referring to  FIG. 6B , the weighted undirected graph  600  now shows the Male gender label values of the nodes  312   a - e  after one iteration of the inferred gender label generator  112 . In this example, the Male gender label values of the nodes  312   a - b  are fixed at “0.00” and “1.00,” respectively. The Male gender label values of the nodes  312   c - e  are now “0.25,” “0.73,” and “0.53,” respectively. The relatively high similarity between the nodes  312   a  and  312   c  as compared to other nodes gives the node  312   c  a Male gender label value (“0.25”) close to the Male gender label value of the node  312   a  (“0.00”). Similarly, the relatively high similarity between the nodes  312   b  and  312   d  as compared to other nodes gives the node  312   d  a Male gender label value (“0.73”) close to the Male gender label value of the node  312   b  (“1.00”). The node  312   e  has slightly higher similarity with the nodes  312   b  and  312   d  leading to a Male gender value (“0.53”) that leans toward Male. 
     Referring to  FIG. 6C , the weighted undirected graph  600  now shows the gender label values for the nodes  312   a - e  after two iterations of the inferred gender label generator  112 . The Male gender label value of the node  312   c  has increased by “0.01” to “0.26.” The Male gender label value of the node  312   d  has increased by “0.01” to “0.74.” The Male gender label value of the node  312   e  has increased by “0.01” to “0.54.” In some implementations, the inferred gender label generator  112  may stop processing a graph after the change in each gender label value is below a particular threshold, such as “0.01.” 
     Referring to  FIG. 6D , the weighted undirected graph  600  now shows the gender label values for the nodes  312   a - e  after four iterations of the inferred gender label generator  112 . The Male gender label value of the node  312   d  has increased by “0.01” to “0.75.” The Male gender label values of the nodes  312   a - e  have now converged within “0.01.” The inferred gender label generator  112  stops processing the weighted undirected graph  600  and outputs the Male gender label values. Alternatively, the inferred gender label generator  112  may stop processing after a predetermined number of iterations, such as five iterations. 
     In some implementations, the inferred gender label generator  112  infers Female gender label values for the nodes  312   a - e  in addition to the Male gender label values previously described. The inferred gender label generator  112  can compare and/or combine the Male and Female gender label values to determine a gender for the faces  302   a - e  associated with the nodes  312   a - e . For example, if the Male gender label value of a node is larger than its Female gender label value than the corresponding face is determined to be Male and vice versa for Female. 
       FIG. 7A  shows an example of a table  700  that stores gender values for the faces  302   a - e . The weighted undirected graphs  310  and  600  may stored in the data storage  110  or structured in a memory module as tables, such as the table  700 . In particular, the table  700  includes an identifier  702  for each of the faces  302   a - e , an indication  704  of where similarity scores to other faces can be found, a Male gender label value  706 , and a Female gender label value  708 . The entry in the table  700  for Face A indicates that the similarity scores associated with face can be found in Table A. 
       FIG. 7B  shows an example of a table  710  that stores similarity scores between a first face and other faces. In particular, the table  710  represents Table A, which includes the similarity scores associated with Face A from the table  700 . The table  710  includes an identifier  712  for each of the associated faces and a corresponding similarity score  714 . 
       FIG. 8  shows an example of a weighted undirected graph  800  including dummy labels. In certain implementations, dummy labels can be used in the weighted undirected graph  800  to reduce effects of distant neighboring nodes. When the label value of a node is determined based on, for example, the label with the greatest label value, the weight assigned to the dummy label can be ignored and the remaining weights used in the determination. For example, the dummy labels&#39; contribution can be removed from the calculation of the label values at the end of the algorithm (e.g., after the label values have reached a steady state or a specified number of iterations have occurred). 
     In certain implementations, dummy labels can be used in all of the graphs generated by the inferred label generator. In other implementations, a user may specify for which graph(s) the dummy labels may be used. 
     The example of dummy labels shown here associates a dummy node with each of the nodes  312   a - e  in the weighted undirected graph  800 . In other implementations, dummy labels are assigned to a small number of nodes, such as nodes that are not associated with initial gender label values. 
       FIG. 9  is a schematic diagram of a generic computing system  900 . The generic computing system  900  can be used for the operations described in association with any of the computer-implement methods described previously, according to one implementation. The generic computing system  900  includes a processor  902 , a memory  904 , a storage device  906 , and an input/output device  908 . Each of the processor  902 , the memory  904 , the storage device  906 , and the input/output device  908  are interconnected using a system bus  910 . The processor  902  is capable of processing instructions for execution within the generic computing system  900 . In one implementation, the processor  902  is a single-threaded processor. In another implementation, the processor  902  is a multi-threaded processor. The processor  902  is capable of processing instructions stored in the memory  904  or on the storage device  906  to display graphical information for a user interface on the input/output device  908 . 
     The memory  904  stores information within the generic computing system  900 . In one implementation, the memory  904  is a computer-readable medium. In one implementation, the memory  904  is a volatile memory unit. In another implementation, the memory  904  is a non-volatile memory unit. 
     The storage device  906  is capable of providing mass storage for the generic computing system  900 . In one implementation, the storage device  906  is a computer-readable medium. In various different implementations, the storage device  906  may be a floppy disk device, a hard disk device, an optical disk device, or a tape device. 
     The input/output device  908  provides input/output operations for the generic computing system  900 . In one implementation, the input/output device  908  includes a keyboard and/or pointing device. In another implementation, the input/output device  908  includes a display unit for displaying graphical user interfaces. 
     The features described can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. The described features can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. 
     Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits). 
     To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer. 
     The features can be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a LAN, a WAN, and the computers and networks forming the Internet. 
     The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a network, such as the described one. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other 
     Although a few implementations have been described in detail above, other modifications are possible. For example, image or facial labels other than gender may be used, such as ethnicity or complexion. 
     In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other implementations are within the scope of the following claims.