Source: http://www.google.com/patents/US5870138?ie=ISO-8859-1&dq=5,832,511
Timestamp: 2014-07-11 07:45:18
Document Index: 564913280

Matched Legal Cases: ['arts 31061', 'art 31061', 'art 31066', 'art 31067', 'arts 31151', 'arts 31511', 'art 31151', 'art 31153', 'art 31152']

Patent US5870138 - Facial image processing - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA camera (10) captures a subject facial image. A unit (30) generates a tracking signal containing feature extraction data. In conjunction with a memory store (40), the unit (30) generates an output signal representing a substitute face having the features of the subject face. To generate the tracking...http://www.google.com/patents/US5870138?utm_source=gb-gplus-sharePatent US5870138 - Facial image processingAdvanced Patent SearchPublication numberUS5870138 APublication typeGrantApplication numberUS 08/615,360Publication dateFeb 9, 1999Filing dateMar 14, 1996Priority dateMar 31, 1995Fee statusPaidPublication number08615360, 615360, US 5870138 A, US 5870138A, US-A-5870138, US5870138 A, US5870138AInventorsAnthony Smith, Hiroshi Sako, Alistair Sutherland, Masahiro AbeOriginal AssigneeHitachi, Ltd.Export CitationBiBTeX, EndNote, RefManPatent Citations (5), Non-Patent Citations (8), Referenced by (129), Classifications (16), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetFacial image processingUS 5870138 AAbstract A camera (10) captures a subject facial image. A unit (30) generates a tracking signal containing feature extraction data. In conjunction with a memory store (40), the unit (30) generates an output signal representing a substitute face having the features of the subject face. To generate the tracking signal, the facial area is detected (3103, 3107-8) initially and used in location of the mouth (3102, 3110-3114) and eyes (3104-6, 3115-3118). Only the H and S components are used for detecting the facial area; and only the S and V components for the mouth within the facial area. A face vector may be generated (50) using the tracking signals.
We claim: 1. An image processing method comprising the steps of receiving a subject facial image signal, generating a feature extraction tracking signal, and processing said tracking signal to provide a processed output signal, wherein:said processing step comprises the sub-steps of:generating an image signal representing a substitute face; and modifying said substitute face image signal in real time according to the tracking signal to generate an output signal representing the substitute face with facial features of the subject face by converting the tracking signal to a facial characteristic signal representing positional characteristics of facial features, the facial characteristic signal being a vector signal wherein pixel change data in an update image memory is selected by comparison of current and previous characteristics signals. 2. A method as claimed in 1 wherein the output signal is transmitted from an output image memory which is modified by overwriting pixel values transferred from an update image memory in response to the tracking signal, the update image memory storing pixel changes between frames the update transfers being controlled by reference to a lookup table having pointers to addresses in the update image memory.
8. A method as claimed in claim 1, wherein the tracking signal is generated by:generating a subject facial image signal in H,S,V format; passing at least two components of said H,S,V format signal through a band pass filter; mapping the filter output signals over the subject facial image pixel area; and determining feature locations within the pixel area according to the mapping of the filter output signals. 9. A method as claimed in claim 8, wherein the step of determining feature locations comprises the sub-steps of detecting the facial area and subsequently detecting mouth and eye areas within the detected facial area.
15. An image processing method comprising the steps of:receiving a subject facial image signal; extracting features in the signal to generate a tracking signal; transforming the tracking signal to a facial characteristic signal representing positional characteristics of features of the subject face; writing a primeval substitute image to an output image memory; and modifying the output image memory by overwriting pixel values transferred from an update image memory, the facial characteristic signal being a vector signal wherein pixel change data in said output image memory is selected by comparison of current and previous characteristics signals, said transfers being controlled in response to the facial characteristic signal. 16. An image processing apparatus comprising means for receiving a subject facial image signal, means for generating a feature extraction tracking signal, and means for processing said tracking signal to provide a processed output signal, said processing means comprising means for:generating an image signal representing a substitute face; and modifying said substitute face image signal in real time according to the tracking signal to generate an output signal representing the substitute face with facial features of the subject face by converting the tracking signal to a facial characteristic signal representing positional characteristics of facial features, the facial characteristic signal being a vector signal wherein pixel change data in an update image memory is selected by comparison of current and previous characteristic signals. 17. An image processing apparatus as claimed in claim 16, wherein said processing means comprises an output image memory, an update image memory and means for modifying said output image memory by transferring pixel values from the update image memory in response to the tracking signal.
22. An image processing apparatus comprising:means for receiving a subject facial image signal; means for extracting features in the signal to generate a tracking signal; and processing means comprising:means for transforming the tracking signal to a facial characteristic signal representing positional characteristics of features of the subject face; an output image memory; means for writing a primeval substitute image to the output image memory; an update image memory; and means for modifying the output image memory by transferring pixel values from the update image memory to the output image memory in response to the facial characteristic signal. 23. An image processing method comprising the steps of receiving an input image signal and generating a feature extraction tracking signal, characterised in that:the input image signal is in H,S,V format; a facial area location signal is generated by passing at least part of the input image signal through a band pass filter and analyzing the output of the filter; a mouth location signal is generated by passing at least part of the input image signal through a band pass filter and analyzing the output of the filter within the facial pixel area according to the facial area location signal; eye location signals are generated by processing at least part of the input image signal within the facial pixel area according to the facial area location signal; and the facial area location, mouth location and eye location signals are outputted as output tracking signals. 24. A method as claimed in claim 23, wherein only two of the H,S,V input image components are used for generation of the facial area location signal.
36. An image processing apparatus comprising:means for receiving an input image signal in H,S,V format; a facial area band pass filter; means for passing at least part of the input image signal through the facial area band pass filter and analyzing the output of the filter to generate a facial area location signal; a mouth location band pass filter; means for passing at least part of the input image signal through the mouth location band pass filter and for analyzing the output of the filter within the face pixel area according to the facial area location signal; processing means for processing at least part of the input image signal within the facial pixel area according to the facial area location signal to generate eye location signals; and means for outputting said facial area location, mouth location, and eye location signals as output tracking signals. 37. An apparatus as claimed in claim 36, wherein only the H and S components of the input image signal are passed through the facial area band pass filter.
FIELD OF THE INVENTION The invention relates to an image processing method and apparatus and more particularly to a method and apparatus which can receive a subject facial image signal, generate a feature extraction tracking signal representing locations of facial features of the subject and provide an output signal.
PRIOR ART DISCUSSION In the paper "Realtime Facial Image Recognition in Unconstrained Environment for Interactive Visual Interface" by Hasegawa et al published in ACCV '93 Asian Conference on Computer vision, November 23-25, Osaka, Japan pp. 763-766 a system is outlined in which features such as the eyes and the nose are extracted as edges. The tracking signal may be processed for integration of facial features or for monitoring eye contact. Because feature extraction involves monitoring feature edges, the amount of useful information would appear to be limited. Little technical information is given in the paper to describe how the system operates. However, it is mentioned that RGB color information is calculated. The use of RGB color information generally leads to high complexity in the image processing circuits.
OBJECTS OF THE INVENTION One object of the invention is to provide a method and apparatus to output a tracking signal which is of more benefit for down-stream processing with a wider range of applications.
SUMMARY OF THE INVENTION The invention provides an image processing method comprising the steps of receiving a subject facial image signal, generating a feature extraction tracking signal, and processing said tracking signal to provide a processed output signal, wherein:
BRIEF DESCRIPTION OF THE DRAWINGS The invention will be more clearly understood from the following description of some embodiments thereof, given by way of example only with reference to the accompanying drawings, in which:
DETAILED DESCRIPTION Referring to the drawings, and initially to FIG. 1, there is shown an image processing apparatus of the invention, indicated generally by the reference numeral 1. The apparatus 1 comprises a camera 10 and an output device, namely a video monitor 20. Between these two devices, there is a face changer unit 30 which is connected at its input to the camera 10 and at its output to a video multiplexer 60(a). The face changer unit 30 is also connected to provide an output signal to a video multiplexer 60(b), the output of which is connected to the video monitor 20. Accordingly, the output of the face changer may be directed via the video multiplexer 60(a) to a videophone interface 70 or directly to the monitor 20. The apparatus 1 also comprises a memory store 40 which is connected to the face changer unit 30. The memory store 40 may receive inputs via the videophone interface 70 and a control logic unit 50 or from the video multiplexer 60(b). The camera 10 is also connected to the video multiplexer 60(a) to by-pass the face changer unit 30.
In operation, the address generator initialises the output image memory 402 with the primeval image by loading the primeval image into the output image memory 402. The multiplexers 401 and 408 by use of control signals Cntrl3, Cntrl4, and Cntrl5 load the primeval image from the primeval image memory 407 into the output image memory 402. Initially, the address generator 409 is loaded with the start location of the primeval image memory 407, and using a CLK generates every pixel address until all data is transferred from the primeval image memory into the output image memory 402. After initialization, all subsequent image data is provided by the update image memory 406. The data stored in the update image memory 406 comprises data elements, each having three fields. The first field is a single bit wide and is used to indicate if the current data is the last in the sequence of pixels to update the image. The second field is the pixel address which is 16 bits wide if an image of 256�256 is being used. The third field is 24 bits wide and represents the RGB color value of the pixel.
The purpose of the smoothing device 3111 shown in FIG. 7(d) is to aid mouth position detection in proceeding image processing by the devices 3112-3114. It will be noted that the face position detection stage (3107-3109) and the mouth position detection stage (3110-3114) share several common tasks, namely XY Projection, find max and search for bounding box. However, the mouth position detection stage includes two extra tasks which are not shown in the face position detection, namely crop picture and smoothing. The purpose of the crop picture device 3110 is explained above. The reason that smoothing is not present in the face position detection derives from the face that the task being undertaken is facial parts identification and position location. This implies that the face will occupy a large area in the input image. In any image processing task there is a level of background noise due to a variety of factors, for example, inaccuracies in the conversion of analogue data to digital data, stroboscopic effects from foreign lighting sources, light reflection from glasses, etc. These add noise to the processed image. In the detection of the facial area, since the skin will cover a large percentage of the input image, there is a considerable number of pixels which will be identified as belonging to the skin. Therefore, the background noise will have little or no effect on the results obtained from the face position detection. However, the mouth occupies a much smaller area, and therefore the background noise will have a much greater effect on obtaining correct results from the mouth position detection stage. The probability that a mouth pixel is mistaken for a skin pixel and vice-versa is high and affects mouth area detection. However, in the case of face position detection the fact that a mouth pixel is mistaken as a skin pixel actually helps in the location of the facial area, since the mouth area lies within the face area. However, the opposite applies for the mouth position detection. To help overcome this problem the image is smoothed before performing further image processing steps. It is assumed that the background noise is random in nature and will occur randomly over the image, whereas the mouth pixel recognition is highly concentrated in a single area. By averaging over an area, the effects of the background noise can be reduced while enhancing the areas where recognition is highly concentrated. The principle behind the device 3111 is to average all pixels within an 8�8 area, and place the result at the central pixel point. The operation of this circuit and its underlying principles will be understood to those skilled in the art. The resulting image is a smoothed representation of the input image.
The purpose of the device 3107 shown in FIG. 7(e) is to perform an XY projection on the image which is outputted from the device 3103 to effectively map the filter output over the pixel area. The device 3107 can be divided into two sections which operate in the same fashion, the left hand side which evaluates the X projected data, and the right hand side which evaluates the Y projected data. The circuit comprises a 256�16 bit SRAM which is used to store the X projected data, a multiplexer to arbitrate access to the databus of the SRAM, a multiplexer to arbitrate access to the address bus of the SRAM, an adder to perform additions on the projected data, and a register to act as an intermediate data store. The circuit functions in the following manner: It is assumed that the SRAM can have all bits set to zero, i.e. the SRAM can be cleared, at the beginning of every XY projection, however, this function is not shown in the diagram. It is also assumed that the maximum image size is 256�256 pixels, however, to those skilled in the art, it is possible to adapt the circuits to handle large images. Pixel data is inputted into the circuit through I/P pixel data, with the address of each pixel being inputted through Row Addr and Column Addr. It is assumed that the Select line is set so that Row Addr signals impinge upon the SRAM, and that the bi-directional buffer is configured to allow data to be read from the SRAM into the ADDER. The Row Addr reads the present X projection value from the SRAM into the ADDER circuit. The ADDER adds together the data from the SRAM and that of the I/P PIXEL DATA and puts the result into the REGISTER. The bi-directional buffer is then configured to write data from the REGISTER into the SRAM so that the new result is stored. The next pixel value is then inputted into the circuit with the new Row Addr signal being used to select the appropriate X storage location. The process is repeated until all pixels in the image have been processed. By changing the select switch to allow the External Row Addr to impinge upon the SRAM it is possible to read out the final X projection values. The operation of the Y projection is carried out in parallel to the X projection.
The normalize grey scale device 3106 is shown in circuit form in FIG. 7(i). The purpose of this stage is to translate the input image in such a way that it uses the full range of possible values, namely 0 to 255. The device 3105 processed the image and found the maximum values. In an 8 bit grey scale representation the minimum value possible is 0, and the maximum value possible is 255. However, results from the device 3105 will indicate that from frame to frame the maximum and minimum values found will not be the maximum and minimum possible. Therefore, it is advantageous that a method be devised that changes the input image so that it fits the full range of values. The simplest method, as shown in FIG. 7(i) is that of a look-up table, 31068, which for an 8 bit input and 8 bit output requires a 256�8 bit memory. This look-up table must be programmed frame by frame, as the maximum and minimum values will change frame by frame. The algorithm for programming the look-up table is as follows: ##EQU1## where the values Max and Min refer to the values of MAX VALUE and MIN VALUE calculated by the device 3105. Max and Min must be between the values of 0 and 255, and Max >Min. FIG. 7(i) shows that the circuit of the device 3106 is made up of devices 31061-31069. The circuit has two modes of operation. The first where the coefficients of the look-up table are calculated and the other where these coefficients are to convert the input image into the normalized output image. The parts 31061-31067 are involved with the calculation of the coefficients which are stored in the SRAM 31068. The data is transformed by allowing the PIXEL DATA to impinge upon the SRAM as the address by setting the SELECT control signal to the correct state. This control signal is under the control of the control unit 50. At the start of each frame all locations in the LUT are set to zero and the MIN VALUE is loaded into a counter, indicated as the part 31061. The MIN VALUE along with MAY, VALUE are obtained from the device 3105. The counter is loaded using a LOAD control signal from the control logic unit 50. The output of the counter is inputted to a comparator CMP, which compares the counter value with the MAX VALUE. If the value of the counter is greater than MAX VALUE then this indicates that all coefficients have been loaded into look-up table and that the normalization process can start. The comparator CMP outputs a control signal named FINISHED which is sent to the control unit 50. The coefficient calculation can be divided into three steps. In the first step two calculations occur in parallel, namely,
______________________________________(a)    MIN VALUE - x      where x is the                     present counter value(b)    MAX VALUE - MIN VALUEthen,(c)    CONST � (Result of 1)                     using part 31066then,(d)    (Result of 3)/(Result of 2)                     using part 31067______________________________________
The device 3115 has parts 31151-31156. The parts 31511 and 31153 are 16�16 8 bit correlators, part 31151 performing the P(i,j)T(i,j) and part 31153 performing 31153 performing the P(i,j)2. The part 31152 is an 256�8 SRAM which is used as a lookup table to convert the input image pixel values to their squared values before correlation. This is required so that numerical accuracy is maintained throughout the correlation process.
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