WO2004003802A2 - Measurement of content ratings through vision and speech recognition - Google Patents

Measurement of content ratings through vision and speech recognition Download PDF

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Publication number
WO2004003802A2
WO2004003802A2 PCT/IB2003/002951 IB0302951W WO2004003802A2 WO 2004003802 A2 WO2004003802 A2 WO 2004003802A2 IB 0302951 W IB0302951 W IB 0302951W WO 2004003802 A2 WO2004003802 A2 WO 2004003802A2
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WO
WIPO (PCT)
Prior art keywords
customer
detection
product
speech
image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2003/002951
Other languages
English (en)
French (fr)
Inventor
Srinivas Gutta
Antonio Colmenarez
Miroslav Trajkovic
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
US Philips Corp
Original Assignee
Koninklijke Philips Electronics NV
US Philips Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics NV, US Philips Corp filed Critical Koninklijke Philips Electronics NV
Priority to EP03761741A priority Critical patent/EP1520242A1/en
Priority to JP2004517151A priority patent/JP2005531080A/ja
Priority to AU2003247000A priority patent/AU2003247000A1/en
Publication of WO2004003802A2 publication Critical patent/WO2004003802A2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/29—Arrangements for monitoring broadcast services or broadcast-related services
    • H04H60/33—Arrangements for monitoring the users' behaviour or opinions

Definitions

  • the present invention relates generally to vision and speech recognition, and more particularly, to methods and devices for measuring customer satisfaction through vision and/or speech recognition.
  • manufacturers and vendors of the displayed products often want information that they'd rather not reveal to the participants, such as characteristics like gender and ethnicity. This type of information can be very useful to manufacturers and vendors in marketing their products. However, because the manufacturers perceive the participants as not wanting to supply such information or be offended by such questioning, the manufacturers and vendors do not ask such questions on their product questionnaires. Therefore it is an object of the present invention to provide methods and apparatus for automatically measuring a customer's satisfaction of a product, service, or content.
  • a method for measuring customer satisfaction with at least one of a service, product, and content comprising: acquiring at least one of image and speech data for the customer; analyzing the acquired at least one of image and speech data for at least one of the following: (a) detection of a gaze of the customer; (b) detection of a facial expression of the customer; (c) detection of an emotion of the customer; (d) detection of a speech of the customer; and (e) detection of an interaction of the customer with at least one of the service, product, and content; and determining customer satisfaction based on at least one of (a) - (e).
  • the method further comprises determining at least one of a gender, ethnicity, and age of the customer from the at least one of image and speech data.
  • the acquiring preferably comprises identifying the customer in the image data.
  • the identifying preferably comprises detecting a face in the image data.
  • the identifying comprises classifying objects in the image data as people and non-people.
  • the detection of a gaze of the customer preferably comprises at least one of determining if a direction of the detected gaze is towards at least one of the service, product, and content and the duration of the gaze towards at least one of the service, product, and content.
  • the detection of a facial expression of the customer comprises determining whether the detected facial expression is one of satisfaction or dissatisfaction.
  • the method preferably further comprises detecting whether the gaze of the customer is towards at least one of the service, product, and content at a time when the facial expression is detected and wherein the determining of the customer satisfaction is at least partly based thereon.
  • the detection of an emotion of the customer is at least partly based on the detection of at least one of the speech and facial expression of the customer.
  • the detection of an emotion of the customer preferably comprises detecting an intensity of the emotion of the customer.
  • the detecting of an intensity of emotion is at least partly based on the detection of at least one of the speech and facial expression of the customer.
  • the detecting of a speech of the customer preferably comprises detecting specific phrases of the recognized speech.
  • the detecting of a speech of the customer comprises detecting emotion in the recognized speech.
  • the detection of an interaction of the customer with at least one of the service, product, and content preferably comprises detecting a physical interaction with at least one of the product, service, and content. Also provided is an apparatus for measuring customer satisfaction with at least one of a service, product, and content.
  • the apparatus comprising: at least one of a camera and microphone for acquiring at least one of image and speech data for the customer; and a processor having means for analyzing the acquired at least one of image and speech data for at least one of the following: (a) detection of a gaze of the customer; (b) detection of a facial expression of the customer; (c) detection of an emotion of the customer; (d) detection of a speech of the customer; and (e) detection of an interaction of the customer with at least one of the service, product, and content; wherein the processor further has means for determining customer satisfaction based on at least one of (a) - (e).
  • the processor further has means for determining at least one of a gender, ethnicity, and age of the customer from the at least one of image and speech data.
  • a computer program product for carrying out the methods of the present invention and a program storage device for the storage of the computer program product therein.
  • Figure 1 illustrates schematic of a preferred implementation of an apparatus for carrying out the methods of the present invention.
  • FIGS. 2a and 2b illustrate a flowchart showing a preferred implementation of a method of the present invention.
  • Apparatus 100 includes at least one, and preferably several cameras 102 having a field of view sufficient to capture image data within a predetermined area of a displayed product, service, or content 104.
  • the term camera is used in its generic sense to mean all image capturing devices.
  • the cameras 102 are preferably digital video cameras, however, they also may be analog video cameras, digital still image cameras and the like. If an analog camera is used, its output must be appropriately converted to a digital format.
  • the cameras 102 can be fixed or have a pan, tilt, and zoom capability.
  • the apparatus also includes at least one microphone 106 for capturing speech data from the predetermined area.
  • the microphone 106 is preferably a digital microphone, however, other types of microphones can also be utilized if the output signal thereof is appropriately converted to a digital format.
  • the term microphone is used in its generic sense to mean all sound capturing devices.
  • the cameras 102 and microphone 106 are useful in acquiring image and speech data for a customer 108a, 108b or other objects 109 within the predetermined area. Although, either a microphone 106 or at least one camera 102 is necessary for practicing the methods of the present invention, it is preferred that both are utilized.
  • the term "customer" refers to any person detected in the image and/or speech data within the field of view/sound of the cameras 102 and microphone 106.
  • Apparatus 100 also includes a processor 114, such as a personal computer.
  • the image and speech recognition means 110, 112, although shown in Figure 1 as separate modules, are preferably implemented in the processor 114 to carry out a set of instructions which analyze the input image and speech data from the cameras 102 and microphone 106.
  • the processor 114 further has means for determining at least one of a gender, ethnicity, and age of the customer 108a, 108b from the captured image and/or speech data.
  • the apparatus 100 also includes an output means 116 for outputting a result of the analysis by the processor 114.
  • the output means 116 can be a printer, monitor, or an electronic signal for use in a further method or apparatus.
  • Figures 2a and 2b illustrate a flowchart showing a preferred implementation of a method to be preferably carried out by apparatus 100, the method being generally referred to by reference numeral 200.
  • the method 200 measures customer satisfaction with at least one of a service, product, and content (collectively referred to herein as a "product").
  • the product can be displayed in a public area, such as a shopping area in which the product (e.g., a consumer product) is displayed within the predetermined area or in a private area in which the product (e.g., content such as a television program) is being viewed within the predetermined area.
  • a public area such as a shopping area in which the product (e.g., a consumer product) is displayed within the predetermined area or in a private area in which the product (e.g., content such as a television program) is being viewed within the predetermined area.
  • image and speech data are acquired for the predetermined area by the cameras 102 and/or microphone 106.
  • the customer(s) 108a, 108b are identified in the image and/or speech data at step 204.
  • the image data is so utilized using any method known in the art for recognizing humans in image data.
  • One such method is where faces are detected in the image data and each face is associated with a person. Once a face is found then it can be safely assumed that a human being exists.
  • An example of the recognition of people in image data by the detection of faces is disclosed in Gutta et al., Mixture of Experts for Classification of Gender, ethnic Origin, and Pose of Human Faces, IEEE Transactions on Neural Networks, Vol. 11, No. 4, July 200.
  • Another method is to classify objects in the image data as people and non-people.
  • Examples of some of the features that can be determined by an analysis of the image and/or speech data are: detection of a gaze of the customer 108a, 108b; detection of a facial expression of the customer 108a, 108b; detection of an emotion of the customer 108a, 108b; detection of a speech of the customer 108a, 108b; and detection of an interaction of the customer 108a, 108b with the product, one or more of which may be utilized to measure a customer's interest/satisfaction in a product.
  • detection of a gaze of the customer(s) 108a, 108b such is preferably carried out at step 206.
  • customer 108a in Figure 1 would be classified as having a gaze towards the product 104, while customer 108b would be classified as having a gaze away from the product 104.
  • the method 200 proceeds along path 208-NO and the customer 208b is not used in the analysis except for his or her apparent non-interest in the product 104 and the method loops back to step 204 where customers continue to be identified in the image data. If a customer 108a is found to have a gaze towards the product 104, the method continues along path 208-YES where other features are detected for that customer 108a.
  • the duration of the gaze can also be detected from the image data. It can be assumed that duration of gaze towards the product is indicative of interest in the product.
  • Methods for detecting gaze in image data are well known in the art, such as that disclosed in Rickert et al., Gaze Estimation using Morphable Models, Proceedings of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan, April 14-16, 1998.
  • the detection of a facial expression of the customer is preferably carried out at step 210 only for those customers 108a that are found to be gazing towards the product 104.
  • the detection of a facial expression of the customer 108a comprises determining whether the detected facial expression is one of satisfaction or dissatisfaction. For instance, the detection of a smile or excited look would indicate satisfaction, while the detection of a frown or perplexed look would indicate dissatisfaction.
  • Methods for detecting facial expressions are well known in the art, such as that disclosed in Colmenarez et al., Modeling the Dynamics of Facial Expressions, CUES Workshop held in conjunction with the International Conference on Computer Vision and Pattern Recognition, Hawaii, USA, December 10 - 15, 2001.
  • the detection of speech is preferably carried out at step 212 and can be useful for not only identifying the customers 108a, 108b in the predetermined area but also in determining a measure of their satisfaction with the product.
  • the detecting of a speech of the customer 108a, 108b can detect specific phrases in the recognized speech. For instance, the recognition of terms “that's great” or “cool” would indicate a measure of satisfaction while the terms “stinks” or “terrible” would indicate a measure of dissatisfaction.
  • the emotion of a detected customer 108a, 108b can be detected. Since customer 108a is gazing at the product, only his or her emotion would be detected.
  • the detection of an emotion of the customer 108a is preferably based on (at least in part) the detection of the speech and/or facial expression of the customer 108a. Furthermore, an intensity of a detected emotion can also be detected. For instance, certain facial expressions, such as an excited look, have a greater emotional intensity than a smile. Similarly, an intensity of emotion can also be detected in the detected speech of the customer 108a, such as where the customer changes his speech pattern (e.g., speaks faster or louder) or uses expletives.
  • a determination that the customer 108a touched the product and possibly played with certain switches or other portions of the product can indicate a measure of satisfaction with the product, particularly when coupled with the detection of a favorable emotion, speech, and/or facial expression.
  • a determination of physical interaction can be made by analyzing the image data from the cameras 102 and/or from feedback from tactile sensors (not shown). Such methods for determining a physical interaction with products are well known in the art. As discussed above, the detection of other features such as gender, gender origin, and age of the customer 108a, 108b may also be made, preferably at step 218.
  • the method 200 can determine that most women are satisfied with a particular product, while most men are either dissatisfied or not interested with the product. Similar marketing strategies may be learned from an analysis of satisfaction and ethnic origin and/or age.
  • customer satisfaction is determined based on at least one of the above- discussed features, and preferably a combination of such features. A simple algorithm for such a determination would be to assign weights to each of the features and calculate a score therefrom which indicates a measure of satisfaction/dissatisfaction.
  • a score that is less than a predetermined number would indicate a dissatisfaction while a score above the predetermined number would indicate a satisfaction with the product 104.
  • Another example would be to assign a point for each feature where a possible satisfaction is indicated, where a cumulative score of the points for all of the features detected over a predetermined number would indicate a satisfaction while a cumulative score below the predetermined number would indicate a dissatisfaction with the product 104.
  • the algorithm may also be complicated and provide for a great number of scenarios and combinations of the detected features.
  • a customer 108a who is detected to be gazing at the product 104 for a long duration of time and whom there is detected a high intensity of emotion in his or her speech and facial expressions would indicate a great satisfaction with the product while a customer 108a who looks at a product with a dissatisfied facial expression and a dissatisfied emotion in his or her speech would indicate little or no interest in the product.
  • a customer 108a who only glances at a product 104 for a short tome and has little or no emotion in his or her speech and facial expression may indicate little or no interest in the product 104.
  • the results of the analysis are output for review, statistical analysis, or use in another method or apparatus.
  • the methods of the present invention are particularly suited to be carried out by a computer software program, such computer software program preferably containing modules corresponding to the individual steps of the methods.
  • a computer software program such computer software program preferably containing modules corresponding to the individual steps of the methods.
  • Such software can of course be embodied in a computer-readable medium, such as an integrated chip or a peripheral device.

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PCT/IB2003/002951 2002-06-27 2003-06-13 Measurement of content ratings through vision and speech recognition Ceased WO2004003802A2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP03761741A EP1520242A1 (en) 2002-06-27 2003-06-13 Measurement of content ratings through vision and speech recognition
JP2004517151A JP2005531080A (ja) 2002-06-27 2003-06-13 視覚及び音声認識を介するコンテンツ格付けの測定
AU2003247000A AU2003247000A1 (en) 2002-06-27 2003-06-13 Measurement of content ratings through vision and speech recognition

Applications Claiming Priority (2)

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US10/183,759 US20040001616A1 (en) 2002-06-27 2002-06-27 Measurement of content ratings through vision and speech recognition
US10/183,759 2002-06-27

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WO2004003802A2 true WO2004003802A2 (en) 2004-01-08

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US (1) US20040001616A1 (enExample)
EP (1) EP1520242A1 (enExample)
JP (1) JP2005531080A (enExample)
CN (1) CN1662922A (enExample)
AU (1) AU2003247000A1 (enExample)
WO (1) WO2004003802A2 (enExample)

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