HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color
  and Texture with a Finger-Mounted Camera
Alexander J. Medeiros Lee Stearns Jon E. Froehlich
We demonstrate the use of DeCAF and Improved Fisher Vector image features to
classify clothing texture. The issue of choosing clothes is a problem for the
blind every day. This work attempts to solve the issue with a finger-mounted
camera and state-of-the-art classification algorithms. To evaluate our
solution, we collected 520 close-up images across 29 pieces of clothing. We
contribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, a
camera small enough to be mounted on the finger, and (2) evaluations of
state-of-the-art recognition algorithms applied to our dataset - achieving an
accuracy >95%. Throughout the paper, we will discuss previous work, evaluate
the current work, and finally, suggest the project's future direction.