Source: http://www.google.com/patents/US20060010582?dq=7,177,838
Timestamp: 2014-03-15 03:46:02
Document Index: 42105688

Matched Legal Cases: ['art 10', 'art 12', 'art 10', 'art 14', 'art 16', 'art 18', 'art 16', 'art 20', 'art 18', 'art 22', 'art 20', 'art 10', 'art 12', 'art 10', 'art 10', 'art 12', 'art 10', 'art 12', 'art 14', 'art 20', 'art 16', 'art 18', 'art 16', 'art 20', 'art 18', 'art 22', 'art 20', 'arts 10', 'art 12', 'art 10', 'art 12']

Patent US20060010582 - Chin detecting method, chin detecting system and chin detecting program for ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA chin detecting method is provided. After detecting a human face and setting a chin detecting window at a lower part of the image, an edge strength distribution is calculated within the chin detecting window and pixels having an edge strength with a threshold value or more are detected based on the...http://www.google.com/patents/US20060010582?utm_source=gb-gplus-sharePatent US20060010582 - Chin detecting method, chin detecting system and chin detecting program for a chin of a human faceAdvanced Patent SearchPublication numberUS20060010582 A1Publication typeApplicationApplication numberUS 11/004,648Publication dateJan 19, 2006Filing dateDec 3, 2004Priority dateDec 5, 2003Also published asWO2005055144A1Publication number004648, 11004648, US 2006/0010582 A1, US 2006/010582 A1, US 20060010582 A1, US 20060010582A1, US 2006010582 A1, US 2006010582A1, US-A1-20060010582, US-A1-2006010582, US2006/0010582A1, US2006/010582A1, US20060010582 A1, US20060010582A1, US2006010582 A1, US2006010582A1InventorsToshinori Nagahashi, Takashi HyugaOriginal AssigneeToshinori Nagahashi, Takashi HyugaExport CitationBiBTeX, EndNote, RefManReferenced by (3), Classifications (13), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetChin detecting method, chin detecting system and chin detecting program for a chin of a human faceUS 20060010582 A1Abstract A chin detecting method is provided. After detecting a human face and setting a chin detecting window at a lower part of the image, an edge strength distribution is calculated within the chin detecting window and pixels having an edge strength with a threshold value or more are detected based on the edge strength distribution. Then an approximated curve is obtained to most match a distribution of each of the detected pixels and a lowermost part of the approximated curve is identified as the lower base of the chin of the human face. Thereby the chin lower base of the human face can be detected automatically, accurately and quickly. Images(10) Claims(10)
DETAILED DESCRIPTION A best mode for carrying out the invention will be described with reference to the drawings. FIG. 1 shows one embodiment of a chin detecting system 100 for a human face according to the invention. As shown in this Figure, the chin detecting system 100 comprises: an image scanning part 10 for scanning a face image G with the human face included therein; a face detecting part 12 for detecting the human face from the face image G scanned in the image scanning part 10 and for setting a face detecting frame F of the human face; a chin detecting window setting part 14 for setting a chin detecting window W with a size including the chin of the human face at a lower part of the face detecting frame F; an edge calculating part 16 for calculating an edge strength distribution within the chin detecting window W; a pixel selecting part 18 for selecting pixels having an edge strength with a threshold value or more based on the edge strength distribution obtained by the edge calculating part 16; a curve approximating part 20 for obtaining an approximated curve to substantially match a distribution of each of the pixels selected in the pixel selecting part 18; and a chin detecting part 22 for detecting a lowermost part of the approximated curve obtained in the curve approximating part 20 as the lower base of the chin of the human face. First, the image scanning part 10 provides a function of obtaining a facial portrait for visual identification attached to, for example, a public ID such as a passport and a driver's license or attached to a private ID such as an employee ID card, a student ID card and a membership card, in other words, obtaining the face image G which has no background and includes largely the human face facing the front as digital image data including each pixel data of R (red), G (green) and B (blue) by using an image pickup sensor such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). More specifically, the CCD of a digital still camera and a digital video camera, a CMOS camera, a vidicon camera, an image scanner, a drum scanner and so on may be used. There is provided a function of analog to digital (A/D) converting the face image G optically scanned in the image pickup sensor and sequentially sending the digital image data to the face detecting part 12. In addition, the image scanning part 10 has a data storing function in which the scanned face image data can be properly stored in a storage device such as a hard disk drive (HDD) and in a storage medium such as DVD-ROM. When the face image is supplied as digital image data through a network and a storage medium, the image scanning part 10 becomes unnecessary or functions as a communication part or an interface (I/F). Next, the face detecting part 12 provides a function of detecting the human face from the face image G scanned in the image scanning part 10 and setting the face detecting frame F at the detected part. This face detecting frame F has a size (an area) including both eyes and the lips of the human face with the nose centered but not including the chin, which will be described later. In addition, although a detection algorithm for the human face by the face detecting part 12 is not especially limited, a conventional method can be utilized as described in the following document, for example: H. A. Rowley, S. Baluja and T. Kanade, �Neural network-based face detection�
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, 1998. According to the technology described in this document, creating a face image of an area including both eyes and the lips of the human face but not including the chin, and training a neural network by using this image, the human face is detected by using the trained neural network. According to the disclosed technology mentioned above, the area from both eyes to the lips is detected as a face image area. The size of the face detecting frame F is not unchangeable and can be increased and decreased depending on the size of the target face image. The chin detecting window setting part 14 has a function of setting the chin detecting window W with a size including the chin of the human face at a lower part of the face detecting frame F set in the face detecting part 20. In other words, there is selected a target area for accurately detecting an outline including the chin lower base of the human face in the following parts from the face image G by using the chin detecting window W. The edge calculating part 16 provides a function of calculating an edge strength distribution within the chin detecting window W. As will be described later, the primary differentiation type edge strength distribution is obtained by using a Sobel edge detection operator or the like. The pixel selecting part 18 provides a function of selecting a pixel having an edge strength with a threshold value or more based on the edge strength distribution obtained by the edge calculating part 16. As will be described later, a candidate pixel obtained by the Sobel edge detection operator by using a secondary differentiation filter (Laplacian filter) is narrowed down by using a sign inversion of the edge. The curve approximating part 20 provides a function of obtaining an approximated curve to match a distribution of each pixel selected in the pixel selecting part 18. As will be described later, the chin outline of the human face is obtained in a curve manner by using a least-square method by a quadratic function as in the following formula. y=a�(x−x 0)2 +b Formula 1 In this formula, y denotes the vertical coordinate, x denotes the horizontal coordinate and x0 denotes the horizontal center of the chin detecting window. Calculating �a� and �b� by using this formula and a least-square method, �b� will express the chin lower base (a<0). The chin lower base detecting part 22 provides a function of detecting a lowermost part of the approximated curve obtained in the curve approximating part 20 as the lower base of the chin of the human face. As shown in FIG. 9, the chin lower base may be expressly provided by attaching a noticeable marker M to the detected chin-lower base. In addition, each of the parts 10 to 22 and so on configuring the chin detecting system 100 is actually realized by a computer system such as a PC which is configured by hardware in the form of a CPU, RAM and the like and which is configured by a special computer program (software) shown in FIG. 3. In the hardware for realizing the chin detecting system 100 as shown in FIG. 2, for example, through various internal/external buses 47 such as a processor bus, a memory bus, a system bus and an I/O bus which are configured by a PCI (Peripheral Component Interconnect) bus, an ISA (Industrial Standard Architecture) bus and so on, there are bus-connected to each other: a CPU (Central Processing Unit) 40 for performing various controls and arithmetic processing; a RAM (Random Access Memory) 41 used for a main storage; a ROM (Read Only Memory) 42 which is a read-only storage device; a secondary storage 43 such as a hard disk drive (HDD) and a semiconductor memory; an output unit 44 configured by a monitor (an LCD (liquid crystal display) or a CRT (cathode-ray tube)) and so on; an input unit 45 configured by an image pickup sensor and so on such as an image scanner, a keyboard, a mouse, a CCD (Charge Coupled Device) and a CMOS (Complementary Metal Oxide Semiconductor); an I/O interface (I/F) 46; and so on. Then, for example, various control programs and data that are supplied through a storage medium such as a CD-ROM, DVD-ROM and a flexible disk (FD) and through a communication network (LAN, WAN, Internet and so on) N are installed on the secondary storage 43 and so on. At the same time, the programs and data are loaded onto the main storage 41 if necessary. According to the programs loaded onto the main storage 41, the CPU 40 performs a specific control and arithmetic processing by using various resources. The processing result (processing data) is output to the output unit 44 through the bus 47 and displayed. The data is properly stored and saved (updated) in the database created by the secondary storage 43 if necessary. A description will now be given about an example of a chin detecting method using the chin detecting system 100 having such a configuration with reference to FIGS. 3-13. FIG. 3 is a flowchart showing an example of a chin detecting method for the face image G to be actually detected. First, as shown in step S101, by the face detecting part 12, after detecting a face included in the face image G from the face image G which has been scanned in the image scanning part 10 and from which the chin will be detected, the face detecting frame F for specifying the detected human face is set. For example, since the image from which the chin will be detected in the invention is limited to the image of one human face as shown in FIG. 6, the location of the human face is first specified by the face detecting part 12 and then the rectangular-shaped face detecting frame F is set on the human face as shown in FIG. 7. In the case of the face detecting frame F as shown in the Figure, although the face detecting frame F has a size (an area) including both eyes and the lips of the human face with the nose centered but not including the chin, the size and shape are not limited to those exemplified if the area does not include the chin part of the human face. Also, although the human face size and the location of a display frame Y in a horizontal direction are within the regulation with regard to each face image G shown in FIGS. 6-9(a), the chin is located too low and is out of regulation. Next, when the face detecting frame F has been set through the above process, moving to step S103 and setting the chin detecting window W having a horizontally long rectangular shape, and the chin location of the human face is specified. The size and shape of the chin detecting window W is not strictly limited. If the chin detecting window W includes an area from the lower lip of a human face to the chin lower base without fail, the size and shape is not especially limited. However, when the chin detecting window W is too large, there are many lines confusingly similar to the chin outline such as the shade of the chin, the wrinkles of the neck and a shirt collar, which increases the time to detect the true edge. When the chin detecting window W is too small, the chin lower base to be detected may not be included in some cases due to the difference between individuals. Therefore, when using the chin detecting window having a horizontally long rectangular shape, the width being wider than a width of the human face and the height being shorter than the width of the human face, it is conceivable that the chin outline including the chin lower base can be reliably captured while eliminating confusingly similar parts such as a shirt collar. Although the chin detecting window W is set by contacting the lower side of the face detecting frame F in the example of FIG. 8, the chin detecting window W does not always have to contact the face detecting frame F. It suffices if a specific positional relationship can be kept between the face detecting frame F and the chin detecting window W. Next, when the chin detecting window W has been set at a target image, moving to step S105 and calculating the luminance (Y) of each pixel within the chin detecting window W and the primary differentiation type edge strength distribution within the chin detecting window W is obtained based on the luminance value by using a primary differentiation type (difference type) edge detection operator typified by a �Sobel edge detection operator� and the like. FIGS. 12(a) and 12(b) show this �Sobel edge detection operator�. In the operator (filter) shown in FIG. 12(a), a horizontal edge is emphasized by adjusting each group of three pixel values located in the left and right rows among eight pixel values surrounding a target pixel. In the operator (filter) shown in FIG. 12(b), vertical and horizontal edges are detected by emphasizing the vertical edge by adjusting each group of three pixel values located in the upper line and lower row among eight pixel values surrounding a target pixel. After calculating the square sum of the result generated in such an operator and calculating the square root, the edge strength can be obtained. However as described above, other primary differentiation type edge detection operators can be applied such as �Roberts� and �Prewitt� in place of the �Sobel edge detection operator�. FIG. 4 shows a relationship between the luminance (vertical axis) of the face image G and the pixel location (horizontal axis) thereof. Since the luminance changes sharply at the edge part in the image such as the chin outline, a parabola-shaped approximated curve can be obtained by using a primary differentiation type (difference type) edge detection operator such as the �Sobel edge detection operator�. Next, when the edge strength distribution within the chin detecting window W has been obtained in such a manner, moving to step S107, a threshold value is calculated from the edge strength distribution. The reason for this is that, as described above, since the edge strength is greatly affected by photographing conditions (lighting conditions) and so on, it is difficult to determine the edge corresponding to the chin outline from the edge strength including other areas. Although the threshold value for determining the pixels is not especially limited, the threshold value may be set at one-tenth of the maximum edge strength detected in the chin detecting window W, for example, and the pixels having a stronger edge than this threshold value are selected as candidate pixels for obtaining the chin lower base. Next, when the threshold value for sorting out the pixel values has been determined, moving to step S111 and selecting only the pixels having the edge strength exceeding the threshold value while scanning in a vertical direction by setting all pixels configuring the upper side of the chin detecting window W as the base point as shown in FIG. 10, and the pixels less than threshold value are eliminated. FIG. 10 shows simply the pixel distribution of the pixels thus selected (exceeding the threshold value). The pixels having the edge strength with a threshold value or more are identified and indicated by scanning the pixels on each line in a non-interlaced manner, that is, scanning in the X-direction within the chin detecting window W from the upper left of the chin detecting window W and moving to the Y-direction sequentially. The reason for scanning from the upper left of the chin detecting window W is that a candidate pixel with a threshold value or more appearing earliest in the Y-direction will be identified as a potential candidate of the chin lower base. Thereby it becomes possible to detect the pixels corresponding to the chin outline effectively. In other words, the reason is that, since an edge which is confusingly similar to the chin outline is stronger at the wrinkles of the neck and a shirt collar located below the actual chin outline than the edge at the upper part of the actual chin outline, the lower edge is desired to be low-prioritized. Next, when the pixels having the edge strength exceeding threshold value have been selected, moving to step S113, a sign inversion of a secondary differentiation type edge is detected in each row in order to narrow down (identify) the pixel having the maximum edge strength in each pixel row (Y-direction) among the selected pixels. When identifying the candidate pixel, it is necessary to consider how sharply the luminance changes. When the luminance changes slowly as shown in FIG. 4, the primary differentiation type Sobel edge strength changes slightly and slowly as shown in FIG. 5(a). When reaching and exceeding the threshold value, the number of candidate pixels increases to lead to an error in determining the chin lower base. For this reason, by detecting the edge sign inversion by using a secondary differentiation type edge detection filter (Laplacian filter) as shown in FIG. 13, one pixel will be determined among plural candidate pixels in each row as shown in FIG. 10 (and FIG. 11). For example, in the case of selecting plural pixels in each row from �a� to �g� as a result of searching the pixels having the edge strength with a threshold value or more as shown in FIG. 10, each uppermost pixel is selected as the candidate pixel configuring the chin outline in rows �a�, �b�, �d�, �f� and �g� in FIG. 11 while each lowermost pixel is selected as the candidate pixel configuring the chin outline in rows �c� and �e� in FIG. 11. After that, when the selected candidate pixel has been finally narrowed down among many pixels exceeding the threshold value, moving to step S115, putting the above-described approximated curve into the distribution of the pixels searched, and the chin lower base will be obtained. When the chin lower base has been detected, attaching a marker M to the chin lower base as shown in FIGS. 9(a) and 9(b) and the entire human face will be moved so that the marker M located at the same location as the proper (regulation) location of chin lower base. In FIG. 9(a), since the chin lower base of the human face is located quite low, the chin lower base can be located at the regulation location by moving the human face vertically upward as shown in FIG. 9(b). Although the image ends at the neck of human as shown in FIG. 9(a) and so on, the image under the neck is assumed to exist actually as it is. As described above, since the lower base of the human face is detected based on the edge strength distribution within the chin detecting window after setting the chin detecting window by using a publicly-known human face detecting method, it becomes possible to detect a robust chin lower base by accurately and quickly detecting the chin lower base of the human face, which is difficult to detect from a face image. Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7643659 *Dec 31, 2005Jan 5, 2010Arcsoft, Inc.Facial feature detection on mobile devicesUS7953253Dec 31, 2005May 31, 2011Arcsoft, Inc.Face detection on mobile devicesUS8149465 *Mar 26, 2009Apr 3, 2012Seiko Epson CorporationColoring image generating apparatus and coloring image generating method for generating a coloring image from an image using an edge intensity frequency distribution of the image* Cited by examinerClassifications U.S. Classification2/425International ClassificationG06K9/00, G06T7/60, A63B71/10, G06T5/00, G06T1/00Cooperative ClassificationG06T7/0083, G06T2207/30201, G06T2207/20012, G06K9/00228, G06T2207/20132European ClassificationG06K9/00F1, G06T7/00S2Legal EventsDateCodeEventDescriptionFeb 23, 2005ASAssignmentOwner name: SEIKO EPSON CORPORATION, JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:NAGAHASHI, TOSHINORI;HYUGA, TAKASHI;REEL/FRAME:015773/0023Effective date: 20050215RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google