Capturing accurate food intake data from participants enrolled in nutrition studies is essential for understanding relationships between diet and chronic disease (1). Numerous methods are employed to assess dietary intake such as food records, 24-hour recalls, or food frequency questionnaires. While each of these techniques is valuable, the error associated with each is unique. The food record requires a motivated participant, is tedious for some, places attention on the act of eating thus altering intake and is difficult for subjects with low literacy skills (2). Interviewing subjects about the previous day’s intake avoids the reactivity involved when recording current intake, but also requires the individual reporting intake to have good recall skills, knowledge of food names and ability to estimate amounts eaten; and requires a well-trained interviewer which makes this a costly process (2, 3). Food frequency questionnaires are limited by food lists and lack of detail regarding food preparation, and require respondents to summarize past intake over many months or the past year. Such instruments are known to contain significant measurement error (4). While all these methods provide valuable information about dietary intake, improving methodology even modestly would advance our knowledge about the influence of food intake on health. FIVR (Food Intake Visual and voice Recognizer), a subproject of the Genes, Environment, and Health Initiative from the National Institutes of Health (RFA-CA-07-032 at www.gei.nih.gov/index.asp), is designed to use new digital photographing technology to reduce measurement error associated with a food record. The intent is to create a tool that would both increase accuracy of intake records and reduce the recording burden for respondents. Using a mobile phone with a camera (Figure 1), the participant will photograph foods both before and after eating. In this way initial portion size is recorded as well as portions left uneaten. The photographs would be used to identify both the types and amounts of foods consumed. This paper briefly describes the technology and techniques involved. Figure 1 Typical Mobile Phone Interface showing (a). operator instruction screen, (b) menu of activities available and (c) camera poised to record meal. Creating sufficiently detailed images Capturing images of meals using a mobile phone presents its own unique challenges. Identifying foods from a picture requires a clear image; the automatic calculation of the amount eaten (volume) requires three or more clear images to be taken by the mobile phone user. Since a single image will not support estimation of food volume, rather 3-dimensional objects must be viewed at more than one angle (5, 6). The three images in Figure 2 are captured from 3 slightly different angles. A calibration object is also required in the images for determination of 3-dimensional size (see Figure 2). The calibration object (fiduciary marker) included in the images in Figure 2 is a card with black and white squares of known size. However, a standard credit card can be used to establish the relationship between size in image pixels and actual size of the object in milliliters. Images are also required before and after the meal is eaten to document the volume of food consumed. Figure 2 Three images captured by moving the camera using the FIVR mobile phone system. Quality of the image hinges on several factors including resolution (roughly indicated by number of pixels per image). Higher resolution (more pixels per image) creates larger files, which makes transferring images slower and more subject to failure, thus testing and refinement of the image details is integral to developing a successful system. Camera focus is critical since the best volumetric estimation is obtained when the three images are in focus and taken with the plate at the same distance from the camera. With fixed focus cameras, the images will be blurred if not taken at the right distance (which is often too great). With auto-focus cameras, the focusing is assured but the distance still must be maintained by the user. Ways to adjust the image to correct for small variations in distance are still being explored.