Abstract:
Methods and apparatus for vision based dating recommendations which analyze face photographs in a members database and recommends potential dating partners. A user selects faces the user finds attractive or unattractive from an objectively diverse group of member photographs. Member faces are mapped and compared with defining data extracted from the attractive and unattractive face selections made by the user to present to the user a ranking of photographs from most to least attractive. The user may continue to select attractive and unattractive face photographs allowing for a more refined and accurate ranking of faces attractive to the particular user. The ranked face data may be combined with narrative compatibility data to present to the user members the user is most likely to find compatible as well as attractive.

Description:
CROSS-REFERENCE 
     This U.S. Patent Application is a continuation-in-part of, and claims the benefits of, U.S. patent application Ser. No. 12/876,197 filed Sep. 6, 2010, entitled “Computerized Face Photograph-Based Dating Recommendation System,” currently pending, the contents of which are incorporated in their entirety by this reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to methods and apparatus for vision based dating recommendations. 
     BACKGROUND OF THE INVENTION 
     The Internet and technology have evolved significantly over past decades. With the speedy development of the internet, applications have grown rapidly such as search engines, blogs, social networking websites, e-commerce websites, etc. 
     In these applications, social networking websites have become more and more popular. These websites enable users to create a profile of their personal information, keep in touch with their friends and even meet new people with similar interests. Some of the social websites are dating websites which users join with the goal of finding suitable persons to date. 
     Dating websites typically contain large numbers of members and member data in regards to which matching and searching is necessary to aid the users in finding suitable persons to date. In attempts to solve the problem, search methods have been created, one of which is disclosed in U.S. Pat. No. 7,657,493. However, these search methods are primarily based on preset narrative search conditions, or non-visual personal attributes, like age, interests, location, salary, etc. While sorting for common interests, educational background, age, salary and other such criteria is a simple database storage and search function, these methods do not provide search options regarding visual attributes such as physical attractiveness. 
     In the area of e-commerce, the structure of e-commerce websites have become more and more complex rendering it difficult for consumers to find the products and service they desire. To address this problem, recommendation methods have been proposed that suggest products and that provide consumers with information to help them decide which products to purchase. Such a recommendation method is disclosed in U.S. Pat. No. 6,370,513. 
     Known recommendation methods in e-commerce identify relationships between different products based upon, for example, customer purchase history. In dating sites, the subjects of the selection process are human beings not products, for which history would typically be unavailable. 
     Conventional e-commerce recommendation methods are not capable of meaningfully addressing the difficulty within dating websites of identifying another member who is attractive to the user of the site. 
     The face is one of the most important and distinctive features of a human being. To locate the similar faces between an input image and each image in a database of faces, some general face recognition methods are used, one of which is disclosed in U.S. Pat. No. 7,430,315. 
     Existing face recognition methods typically only recognize a face and find the similarity to other face images. However, they do not maximize the real behavioral and emotional components of what a user may find attractive or unattractive among the faces throughout the members in the dating website. It is important to note, that similarity is not the same as attraction. Saying a picture is similar to another picture that you like does not measure how much you like the original picture versus the similar picture, it only states that the images are within a range of closeness to being the same or being identical. A user stating he or she likes an image and that another image is not similar to the image the user likes is in no way saying the user does not like the non-similar image, it is only saying that the images are not similar. In fact, the user may also like the non-similar image. This misconception, that because faces are similar the user is likely to be attracted to both, or because faces are dissimilar the user is likely to be attracted to just one and not the other, is an area where current computer face selection methods miss the mark when it comes to dating websites. 
     It is difficult to find people to whom an individual user is attracted by appearance, especially among the large number of members on dating websites. Manual searches are time consuming and impractical. In attempts to solve the problem, face similarity search methods have been created such as the one disclosed in U.S. Pat. No. 7,907,755. However, as with traditional face recognition methods, in this disclosed computerized method a single face image a user selects is compared to other face images to find those most similar to that one image. These results are limited to faces having similar facial characteristics to the single face selected by the user. For example, if a user selects an image of Jennifer Aniston, the user will be presented similar faces to Jennifer Aniston, and not faces similar to the multiple other face types the user may also find attractive. A problem which the instant invention overcomes, is that the aforementioned method starts with a user&#39;s pre-identified notion of one or more individuals selected for similarity, none of which may be similar to face images within the database of members which the user might nevertheless find attractive. In other words, the user may request the system find similar faces to a single query face of Jennifer Aniston, but there may not be any faces that are similar to Jennifer Aniston&#39;s in the members&#39; database, but the members&#39; database may in actuality contain many different face types that the user may find equally attractive but that are not similar to Jennifer Aniston. 
     Further according to the aforementioned method, the approach of allowing additional user selection criteria only adds another narrow search criteria to the process. For example, allowing the user to request similarity for parts of the face, e.g., eyes, nose, mouth, etc., to be searched individually from the whole face makes for a non-comprehensive approach that, like the initial approach of locating similar faces, fails to account for the emotional and subjective manner in which individuals evaluate the attractiveness of others. This conscious partitioning of individual parts of the face (e.g. eyes, nose, mouth, etc.) demonstrates a clear lack of understanding of how the human subconscious perceives faces as attractive. To one user, the same mouth may be found attractive on one face but unattractive on another. Consciously singling out facial parts does not assist in predicting whether a user will find another given face attractive. 
     In addition to a user having faces he or she finds attractive, the user will also have faces he or she finds unattractive. Ignoring the unattractive faces creates its own set of limitations that have yet to be addressed by known face selection methods. 
     The cellular processes of the brain that respond emotionally to a face are subconscious processes which are extremely complex and which respond to the face as a whole, not as a sum of individual face parts. Attraction is an emotional response that is specific to each individual based on each individual&#39;s lifetime of experiences. 
     When using dating sites, finding face types to which a user is attracted but which cannot be identified as words in a profile is often most important to guide users in finding their potential matches among members. Much useful information that is hidden in people&#39;s subconscious perception of another&#39;s face photograph is not used in the website&#39;s search and/or match process, and therefore lost, in conventional face selection methods. 
     Just like the internet and technology&#39;s speedy evolution, our understanding of how the human mind functions has evolved. For example, research indicates the mental process of being attracted or not-attracted to a unique face is a combination of visual recognition followed by emotional response. These two processes occur in separate regions of the brain. A face is seen and recognized as a unique face, then a separate mental process reacts to that face emotionally. Hari, Riitta, Miiamaaria, V. Kujala.  Brain Basis of Human Social Interaction: From Concepts to Brain Imaging  Physiol Rev April 2009 vol. 89 no. 2 453-479. We are able to consciously identify that we see a face, and describe the face we see, but why we have the emotional reaction that follows the recognition is a subconscious, complex process based on a lifetime of experiences starting from birth. Unlike products where the details as to why we like one over another are easy to put in words, the details as to why one face attracts us and another similar face does not is most often a feeling we recognize but cannot explain with words. In conventional recommendation methods, enjoyable and appealing products are recommended. An organizing method in the context of dating sites which can largely reduce search scopes for users is extremely important. But with limited understanding of how the human brain processes face types and emotional response, current vision dating recommendation methods and e-commerce search methods fall short. 
     The method of the subject invention is dependent on the involvement of the individual user, and the individual user&#39;s assessment of a face or faces as attractive or unattractive, and does not in any way rely, as do some of the prior art, on the assessment of any other person regarding face similarity. So, for example, there are no “training users,” or “human assessors”, i.e., no human agents who perform an identifying and scoring function with respect to multiple photographs in order to provide a basis for identifying faces as similar. Rather, the determination of importance in the context of the instant invention is based solely on the individual user&#39;s selections of attractive and unattractive, and not based on any other person&#39;s assessments whatsoever. 
     SUMMARY OF THE INVENTION 
     In consideration of the above-mentioned problems with conventional methods and in order to accomplish a date recommendation service using image information, the present invention provides a vision based dating recommendation method which can identify or select members images to which an individual user is attracted and not-attracted. The present invention: (1) provides an individual user with an objectively diverse multiple image seed group, (2) the ability to make simultaneous multiple selections of member images for data extraction, (3) employs the user selections of both attractive and unattractive face images to determine an importance score, (4) mines and organizes members in the database by their narrative profiles into two or more subgroups, (5) within each subgroup, ranks members in the database by their image data from most to least attractive to the user, and (6) optionally provides the user with additional attractive and/or unattractive face image selections for continued refinement of date recommendation results. 
     A first aspect of the present invention is a vision based dating recommendation method comprising an objectively diverse multiple image seed group generated from face images of members independent of any user input. The word “members”, as used with respect to the instant invention, refers to individuals available for dating other than the user. As used herein and explained in greater detail below, the phrase “objectively diverse seed group” refers to those face images which, after mapping, are determined to be the most mathematically diverse from the others. “Mapping” refers to assigning numerical values to various predetermined aspects of facial features so that the facial features of one face image can be objectively compared and contrasted with the analogous facial features of another face image. 
     A second aspect of the present invention is a vision based dating recommendation method comprising an individual user making simultaneous selections of multiple face images the user finds attractive or unattractive within the seed group, and simultaneously submitting these selections for scoring; measuring and scoring of the simultaneously submitted face images which the user found attractive or unattractive; and scoring the data importance between attractive selected images and unattractive selected images. As used herein, a “seed group” refers to a sample of face images selected from an entire database of images to be presented to the user for user selection. 
     A third aspect of the present invention is a vision based dating recommendation method comprising scoring of all members&#39; images in the database based on the seed group attractive and unattractive selections made by the user and the importance scores resulting from the seed group selections. As used herein, “scoring” of individual face images refers to a process of applying mapped face data to an algorithm in order to assign an order or rank to the face images within the image database. 
     A fourth aspect of the present invention is a vision based dating recommendation method comprising using data mining of the user&#39;s and all members&#39; narrative (non-image) profile input in the database, and matching and organizing members into two or more narrative subgroups. As used herein, the term “mining” refers to the process of extracting relevant data/information from stored data. 
     A fifth aspect of the present invention is a vision based dating recommendation method comprising ranking and organizing each member&#39;s face image in the database, based on their measured score, from most attractive to least attractive within each of the narrative subgroups. 
     A sixth aspect of the present invention is a vision based dating recommendation method comprising presenting to the user the scored, matched, ranked and organized narrative and image data as a members dating recommendation match list. 
     A seventh aspect of the present invention is vision a based dating recommendation method comprising a continuation of the members&#39; image scoring based on the selections made by the user in the seed group wherein the user continues to make selections within the members recommendation match list of who they find attractive or unattractive, after which the member images within the member recommendation match list are re-scored, reorganized, and then redisplayed for the user from most attractive to least attractive. 
     An eighth aspect of the present invention is a vision based dating recommendation method comprising using a scale of attractiveness that is greater than binary (alphabetical, numerical, symbol scale, or any other kind) for the user to make more precise indications of the gradation of attractiveness and unattractiveness of the displayed member images, e.g., ++/most-attractive, +/somewhat-attractive, −/somewhat-unattractive, −−/most-unattractive. 
     With respect to each aspect of the present invention, a vision based dating recommendation method includes computer vision. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow diagram of a preferred embodiment of the proposed method for vision based dating recommendation using computers and deployed over a network. 
         FIG. 2  provides further detail of the preferred embodiment depicted in  FIG. 1  as a flow chart of the proposed framework and structure for the computer vision based dating recommendation method and system of the present invention. 
         FIG. 3  is a table illustrating the recording of stored data of a user&#39;s or member&#39;s narrative input. 
         FIG. 4  shows part of a member&#39;s sample profile questionnaire. 
         FIG. 5  illustrates a method for mapping face images using facial points on a photograph of a face. 
         FIG. 6  depicts a sample dating recommendation match list output. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention is described below referring to the accompanying drawings, wherein like numerals refer to like or corresponding elements throughout. It should be understood, however, that the drawings and detailed description relating thereto are not intended to limit the claimed subject matter to the particular form or embodiment disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the claimed subject matter. 
     Referring initially to  FIG. 1 , a flow chart of a preferred embodiment of the dating recommendation method of the present invention presented in the context of a dating service using computers and deployed over a network, users  100  access a system server  101  over a public or private network, as for example the Internet. A comprehensive members database  102  stores and updates members&#39; narrative profiles, face images, and or other member registration and profile information. Face images stored in  102  are mapped and stored in a members&#39; processed database  103 , while narrative data stored in  102  are mined and scored and then stored in members&#39; processed database  103 . The mapped face images stored in database  103  are scored using attractive/unattractive importance data (explained below) obtained from user selections, together with mapped difference data (also explained below), and stored in a results database  104 . Data from results database  104  are processed for organization and presentation of member profiles in real time, with output results provided to users  100  through system server  101 . 
     Depicted in  FIG. 2  is a more detailed flow diagram illustrating the general framework and structure of a preferred embodiment of the method of the subject invention depicted in  FIG. 1 . Data from the comprehensive members&#39; database  102  is processed and stored in a members&#39; processed database  103 . For the purpose of the invention, certain data are ignored, e.g., username, email, password, etc., while useful data are processed, e.g., user image, narrative questionnaire answers, age range desired, sex desired, personality information, etc. 
     In a face mapping step  200 , facial data for all images from database  102  are extracted and mapped. Facial data can be extracted by different methods including, Principal Components Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminate Analysis (LDA) or geometric features extraction, but are not limited to the above methods. 
     A narrative scoring step  201  mines, using known data mining technologies, the relationship between different member profiles through their narrative (non-image) input and scores members according to how closely each members&#39; narrative input matches the stated narrative desires of the user as well as how closely the user matches the stated narrative desires of each member. Narrative scoring step  201  scores all members&#39; narrative input and stores that information in the members&#39; processed database  103 . 
     All information obtained from the face mapping step  200  and narrative scoring step  201  is stored in a members&#39; processed database  103 . A seed group generator  202  generates an objectively diverse seed group  203  as described in greater detail below. The interest of the user is best served if seed group  203  is sufficiently large and objectively diverse in order to avoid an image selection process that is narrow. An objectively diverse seed group avoids a common flaw with existing computer vision based recommendation methods used for online dating. The common flaw is using a limited, narrow, or small start group/seed sample/query image. 
       FIG. 3  illustrates a manner of storing ranked data with respect to members&#39; narrative profile input. According to the embodiment of narrative data storage depicted in  FIG. 3 , data points A1, A2, . . . A24 are examples of the narrative attributes from a page of the questionnaire illustrated in  FIG. 4  of a member&#39;s profile. For example, in  FIG. 4 . the content in the “Personality” assessment section of the questionnaire can be regarded as narrative attributes. “Assertive” is coded A1, “Energetic” A2, . . . , “Patient” A24, etc. In  FIG. 4 . each attribute has five possible rankings: “Least Accurate”, “Slightly Not Accurate”, “Medium Accuracy”, “Slightly Accurate”, “Most Accurate”. These five selection rankings can be quantified as 5 numbers from 1 through 5. 
     When a new member registers into the dating website, the member will be required to input his or her narrative data by filling out the questionnaire, a portion of which is illustrated in  FIG. 4  and the user&#39;s questionnaire answers will be processed into data like the A1=2, A2=3, . . . , A24=5 data points depicted in  FIG. 3 , etc. 
     Referring again to  FIG. 2 , seed group generator  202  generates an objectively diverse seed group from among the member face images of members who are within the user&#39;s desired age range and sex, as follows: Seed group generator  202  randomly selects a first face image X 1 , from the members&#39; processed database  103 . Seed group generator  202  selects the next face image in the members&#39; processed database  103  whose mapped face image is furthest from/least like X 1  and adds it to the seed group as X 2 . Generator  202  then selects the next face from database  103  whose mapped face image is furthest from the two seed group faces X 1  and X 2 , and adds it to the seed group as X 3 . Generator  202  then selects the next face from database  103  whose mapped face image is furthest from the three faces, X 1 , X 2  and X 3 , and adds it to the seed group as X 4 , and so on . . . through X n , until the predetermined seed group number (in this embodiment 24) has been selected. Generator  202  thus generates an objectively diverse seed group of 24 member images to be presented to the user as seed group  203  for manual attractive and/or unattractive selection by the user through system server  101 . 
     Through server  101  the user is therefore presented with a group of face images that are as diverse as possible from each other, i.e., the objectively diverse seed group  203 . This allows the invention to insure the broadest possible variety of facial appearances within the seed group and avoids a narrow appearance selection process which would unnecessarily limit the variety of face types presented to the user. In one preferred embodiment, the user can make manual attractive “+” or unattractive “−” selections with respect to each face image in the seed group through system server  101 . 
     Also according to one embodiment, in the event the user does not make the minimum number of attractive “+” or unattractive “−” face image selections required (in this example, the minimum number being 5), an additional seed group population  203 ′ (not illustrated) is presented to the user. The additional seed group population  203 ′ is generated by seed group generator  202  in the same manner as disclosed above, i.e. by selecting X 25  thru X 48 . 
     As illustrated in  FIG. 2 , user  100  accesses the dating website and member database  102  through system server  101 . When registering into the website, the user inputs his/her personal profile which consists of answering narrative (non-image) questions about his/her self and his/her ideal match. The user also uploads his/her own face image to be stored in member database  102 . For example, a user  100  first inputs his profile via a questionnaire. The resulting profile may be, for example: white/Caucasian, male, age 30, 6′1″, outgoing, energetic personality, seeking a female age 25 to 35, etc. In one preferred embodiment, based on the user&#39;s narrative profile input of only the sex and age range of their desired match, seed group generator  202  selects 24 face images that fit that narrative and are objectively diverse in appearance each from the others. These 24 diverse face images are then presented to the user through system server  101  as seed group  203 . 
     Once presented with objectively diverse seed group  203 , the user can make selections among seed group  203  images the user finds attractive “+” or unattractive “−”. The selections are stored in an attractive and unattractive database  204 . The user selections are based on whole face images which minors the way the brain processes emotional responses to faces. Stored attractive “+” and unattractive “−” data from database  204  are processed for datapoint importance scores in step  205 . Datapoint importance scoring is explained in further detail below. 
     In an alternative embodiment, not illustrated, user  100  is asked to make selections from objectively diverse seed group  203  using a scale of attractiveness that is greater than binary (alphabetical, numerical, symbol scale, or any other kind) in order that user  100  may make more precise indications of the gradation of attractiveness and unattractiveness of the displayed seed images. So, for example, user  100  may be asked to rank seed group  203  images as ++/most-attractive, +/somewhat-attractive, −/somewhat-unattractive, −−/most-unattractive. This provides additional information to be used in the importance scoring in step  205  and images scoring in step  207 , and potentially a more accurate or more efficient result. 
     Stored attractive “+” and unattractive “−” data from database  204  and datapoint importance scores from step  205  are stored in an importance scores and attractive and unattractive database  206 . Importance and attractive and unattractive data from  206  are used in step  207  to determine and assign minimum face image distance scores to all images from database  103 . Data processed in  207  is stored in members&#39; face image distance scores database  208 . 
     Data from database  208  and narrative data from  103  are combined in step  209  which first organizes all of the members&#39; narrative profile information from the members&#39; processed database  103  into two or more subgroups ranked according to how closely the members&#39; narratives match the desired narratives of the user and how closely the user&#39;s narrative matches the desired narratives of the members. 
     In one embodiment of the invention, the narrative profile scores can be grouped into 3 subgroups based on matching percentage as, for example, the 100th to the 85th percentile group, the 84th to the 75th percentile group, and the 74th to the 0 percentile group. In another embodiment of the invention, the subgroup percentiles can be set by the user. 
     Within the narrative subgroups, step  209  ranks and organizes the member profiles using only their Face Image Distance Scores from minimum “attractive” face distance score to minimum “unattractive” face distance score, or in other words, from most attractive to least attractive to the user. This avoids the pitfall of existing dating recommendation methods which only rely on the face distance/similarity scores of an individual attractive selected face or query face and wrongfully assuming the furthest from those would be the least attractive. Merely because a face image scores furthest from a face image which has been selected as attractive, does not necessarily mean that the individual user would not find that face attractive. For example, with objectively generated seed group  203 , a first random face X 1  might be selected as attractive by the individual user, but that does not suggest that the next face X 2  could not also be found to be attractive by the user. Conversely, with the objectively generated seed group, a first random face X 1  might be selected as unattractive by the user, but that does not suggest that the next face X 2  could not also be found to be unattractive by the user. 
     The matched, ranked and organized results from step  209  are stored in results database  104 . The stored results data from database  104  are presented as a dating recommendation list in step  210 , as illustrated in one embodiment in  FIG. 6 , to the user  100  via system server  101 . This unique approach provides greater ranking accuracy over existing dating recommendation technologies. 
       FIG. 5  illustrates a method of face mapping through the numerical assignment of data points on a face from which distance scores can be calculated when compared with the corresponding data points on other faces. In scoring step  207  (shown in  FIG. 2 ), the minimum Face Image Distance Score is determined with respect to each face in the members&#39; processed database  103  from both attractive and unattractive selections and datapoint importance scores stored in  206 . A “datapoint” as shown in  FIG. 5 . is a singular face data point on a grid with an X and Y axis used in computer vision face mapping. The Distance Score is the sum of the distances between each datapoint multiplied by the importance of that datapoint. The member Face Image Distance Score is the minimum of the distance scores calculated for an individual member image using the following formula. The user selects photos for attractiveness, i.e., as attractive or unattractive. For however many attractiveness selections the user has made, each member image in the database will be distance scored against each of those user made attractiveness selections individually. So for example: if a user selected 4 images as attractive and 3 images as unattractive, a distance score will be determined for each member image in the database against each of those 7 selected attractive and unattractive images individually, resulting in 7 different distance scores for that member face image, wherein the minimum of those distance scores is assigned to the member as their Face Image Distance Score for later organizing. The formula for determining each distance score is the following:
 
Distance Score=(AttractivenessSelectedImage 1 DataPoint 1 −MemberImage 1 DataPoint 1 )×ImportanceofDataPoint 1 +(AttractivenessSelectedImage 1 DataPoint 2 −MemberImage 1 markDataPoint 2 )×ImportanceofDataPoint 2 +(AttractivenessSelectedImage 1 DataPoint 3 −MemberImage 1 DataPoint 3 )×ImportanceofDataPoint 3  . . . (AttractivenessSelectedImage 1 DataPoint N −MemberImage 1 DataPoint N )×ImportanceofDataPoint N .
 
     The importance of each datapoint is proportional to the statistical differences for that datapoint between the face images selected as attractive and the face images selected as unattractive. More specifically, the importance of a datapoint will be proportional to the difference in the mean and standard deviation of the datapoint of the attractive and unattractive selected face images. For example for any datapoint Z: Datapoint importance Z=Absolute value ((standard deviation of Z for “attractive” photos)−(standard deviation of Z for “unattractive” photos))+Absolute Value ((mean of Z for “attractive” photos−mean of Z for “unattractive” photos)). 
     Or, in layperson&#39;s terms, statistical difference is broken into A and B: A: standard deviation is a measure of how much variance a variable has. To find the standard deviation of datapoint importance Z the standard deviation is found for datapoint Z among all of the “attractive” faces. The standard deviation is found for datapoint Z among all of the “unattractive” faces. A comparison is made from the result deviation score of the “attractive” to the “unattractive”. The importance of the deviation is based on the closeness of the standard deviation from “attractive” to “unattractive” face datapoint Z. The less the deviation between attractive and unattractive face points the less the potential datapoint importance. The mean is measured as well, before determining total importance. B: The mean (average) is found for datapoint Z among all of the attractive “+” faces. The mean (average) is found for datapoint Z among all of the unattractive “−” faces. The difference is calculated between the mean score of the attractive “+” faces and the mean score of the unattractive “−” faces, i.e., subtract one from the other. That difference score along with the standard deviation score, is what determines the datapoint importance Z. 
     The dating recommendation list presentation  210  (as illustrated in one embodiment in  FIG. 6 ) presents the stored organized data from results database  104  to user  100  through system server  101 . User  100  may continue to make attractive “+” and/or unattractive “−” selections from the dating recommendation list presentation  210 . The new attractive “+” and unattractive “−” selections made by user  100  are stored in the attractive and unattractive database  204 . When attractive and unattractive database  204  stores new data, it processes all stored attractive “+” and unattractive “−” data from database  204  for updated datapoint importance scores in step  205 . The process continues the same as above in order to produce an updated dating recommendation list  210 ′. The new dating recommendation list presentation  210 ′ may result in additional attractive and unattractive selections by user  100 , in which case those selections continue through the above described process resulting in another dating recommendation list presentation  210 ′ and the possibility of the user making additional attractive and unattractive selections from that dating recommendation list, in which case the process continues as before. 
     While the vision based dating recommendation method of the present invention has been described in terms of a dating services implemented over a public network, such as the Internet, the method of the present invention can be implemented also over a private network and even without a network. One more steps of the method may be implemented by way of hardware and software, and in most cases many of the steps of the method of the invention will be. Such hardware would include but not be limited to, for example, general-purpose computers, personal computers, special-purposes computers, work stations, servers, data storage devices including all forms of writable media as for example hard drives, and graphic user interfaces, Software would include but not be limited to, for example, software applications, computer programs, and code stored on media. Such hardware and software may be and generally will be integrated into a system and any combination of hardware and software is envisioned to carry out one or more steps of the method of the instant invention. It should also be understood that known communication methods and protocols that itself include all varieties of hardware and software can be used to carry out those steps in which information is communicated to and from the user and between databases, servers and information processors. 
     Further, while specific mapping, statistical, matching or organizing methods have been described above with respect to preferred embodiments, those possessing ordinary skill in the art will appreciate that the invention can be used with other known and understood mapping, statistical, matching or organizing methods without departing from the spirit and scope of the claimed subject matter. 
     In addition, while the invention has been described in terms of a dating recommendation method and service, the invention can also be applied in other contexts where identifying persons or face types that are attractive to one or more given users may be important or useful as, for example, by advertising agencies and public relations companies, casting directors and the like.