# US 7,187,787 B2 · Method and apparatus for facial identification enhancement > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,187,787 B2 - Document ID: US7187787B2 - Status: Granted - Kind: B2 - Application number: 10/635,565 - Filed: 2003-08-07 - Published: 2004-09-16 - Granted: 2007-03-06 - Earliest claimed: 2003-08-07 - Assignees: InteliTrac, Inc. - Inventors: Donald A. Milne; Tianlong Chen; Yi Rui; Yingbin Wang; Jonathon Vu; Yinong Wei - Web page: https://patentgrep.com/patent/US7187787B2 ## CPC - G06V 40/16: Human faces, e.g. facial parts, sketches or expressions - G06F 16/583: using metadata automatically derived from the content - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method and implementation of enhancing a facial recognition process to increase the judgment confidence on identifying a person from a large image database of multiple persons. The method may include reconstructing a database of 2D images having only a small number of images with respect to each person into a database having multiple images, perhaps hundreds or thousands, of each person. The multiple images represent different camera angles or different lighting conditions. The method further includes adding an extra confidence percentage to matching images in the database when multiple images of a person are identified as matching. ## Claims ### Claim 1 A method of finding up to a pre-set number of database images each having at least a pre-set confidence percentage that said database image matches a target facial image, comprising the steps of: reconstructing a first 2D facial image database into a second 2D facial image database, said first 2D facial image database having a first facial image for a person, said second 2D facial image database having a plurality of parsed facial images for said person; choosing a qualification percentage no greater than said pre-set confidence percentage; choosing a qualification candidate number; creating a voting group of people corresponding to parsed images from said second 2D facial image database by finding up to said qualification candidate number of highest matches of said target facial image using a 2D facial recognition algorithm; generating a combined matching confidence percentage for each person in said voting group; creating a final match list for said target facial image by selecting up to said pre-set number of highest matches from said voting group said combined matching confidence percentage for each person in said voting group. ### Claim 2 (depends on 1) A method according to claim 1 wherein said reconstructing step, comprising the steps of: generating a 3D module from one or more images of a person in said first 2D facial image database; generating a plurality of parsed 2D images from said 3D module of said each person in said first 2D facial image database. ### Claim 3 (depends on 2) A method according to claim 2 wherein said plurality of parsed 2D images comprises at least one hundred images. ### Claim 4 (depends on 2) A method according to claim 2 wherein said generating parsed 2D images step comprises the steps of: rotating said 3D module to one or more different angles; and saving a plurality of 2D images corresponding to said one or more different angles. ### Claim 5 (depends on 2) A method according to claim 2 wherein said generating parsed 2D images step comprises the steps of: selecting one or more different angles based upon known angles of said target image; rotating said 3D module to said one or more different angles; and saving a plurality of 2D images corresponding to said one or more different angles. ### Claim 6 (depends on 2) A method according to claim 2 wherein said generating parsed 2D images step comprises the steps of: changing the lighting for said 3D module to a plurality of lighting settings; and saving a plurality of 2D images corresponding to said lighting settings. ### Claim 7 (depends on 6) A method according to claim 6 wherein said step of changing the lighting comprises changing the lighting strength. ### Claim 8 (depends on 6) A method according to claim 6 wherein said step of changing the lighting comprises changing the lighting colors. ### Claim 9 (depends on 6) A method according to claim 6 wherein said step of changing the lighting comprises changing the lighting brightness. ### Claim 10 (depends on 6) A method according to claim 6 wherein said step of changing the lighting comprises changing the lighting hue. ### Claim 11 (depends on 2) A method according to claim 2 wherein said generating parsed 2D images step comprises the steps of: selecting a plurality of lighting settings based upon one or more known lighting settings of said target image; changing the lighting for said 3D module to said plurality of lighting settings; and saving a plurality of 2D images corresponding to said lighting settings. ### Claim 12 (depends on 1) A method according to claim 1 wherein said reconstructing step comprises the steps of: generating said parsed images by rotating a 2D image of a person in said first 2D facial image database, and further saving as 2D images. ### Claim 13 (depends on 1) A method according to claim 1 wherein said second 2D facial image database comprises an identical number of parsed images for each person. ### Claim 14 (depends on 1) A method according to claim 1 wherein said second 2D facial image database comprises a similar number of parsed images for each person. ### Claim 15 (depends on 14) A method according to claim 14 wherein the number of parsed images for each person in said second 2D facial image database are within plus or minus five percent. ### Claim 16 (depends on 1) A method according to claim 1 wherein said qualification candidate number is chosen as an average number of parsed images per person in said second 2D facial image database. ### Claim 17 (depends on 1) A method according to claim 1 further comprising the step of assigning an ID to a plurality of images in said second 2D database corresponding to a single person. ### Claim 18 (depends on 17) A method according to claim 17 further comprising the step of digitizing said plurality of parsed images of said single person into templates. ### Claim 19 (depends on 18) A method according to claim 18 further comprising the step of assigning said ID to said templates. ### Claim 20 (depends on 19) A method according to claim 19 wherein said matches with same ID in said voting group belong to a same person. ### Claim 21 (depends on 19) A method according to claim 19 wherein said combined matching confidence percentage of a person is generated from said matches with same ID of said person. ### Claim 22 (depends on 18) A method according to claim 18 further comprising the step of digitizing said first facial image of said person into a template. ### Claim 23 (depends on 22) A method according to claim 22 further comprising the step of assigning said ID to said templates of said parsed images and to said template of said first facial image of said person. ### Claim 24 (depends on 23) A method according to claim 23, further comprising the step of removing said plurality of parsed images from said second 2D facial image database. ### Claim 25 (depends on 18) A method according to claim 18 wherein said step of creating a voting group of people corresponding parsed images from said second 2D facial image database by finding up to said qualification candidate number of highest matches of said target facial image using a 2D facial recognition algorithm is performed based upon said templates. ### Claim 26 (depends on 1) A method according to claim 1 wherein only said first facial image of a person is listed in said final match list. ### Claim 27 (depends on 1) A method according to claim 1 wherein an extra confidence percentage is chosen for determining said combined matching confidence percentage of a person. ### Claim 28 (depends on 1) A method according to claim 1 wherein said combined confidence percentage of a person in said voting group is determined as the maximum percentage of said person in said voting group plus the number of matches of said person in voting group divided by the said qualification candidate number times the said extra confidence percentage. ### Claim 29 (depends on 1) A method according to claim 1 wherein said combined confidence percentage of a person is set to 100 if said combined confidence percentage is greater than 100. ### Claim 30 (depends on 1) A method according to claim 1 wherein the number of matches in said final match list is zero if the number of said voting group is zero. ### Claim 31 (depends on 1) A method according to claim 1 wherein said combined confidence percentage of a person in said voting group is determined as the maximum percentage of said person in said voting group.