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We demonstrate the use of DeCAF and Improved Fisher Vector image features toclassify clothing texture. The issue of choosing clothes is a problem for theblind every day. This work attempts to solve the issue with a finger-mountedcamera and state-of-the-art classification algorithms. To evaluate oursolution, we collected 520 close-up images across 29 pieces of clothing. Wecontribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, acamera small enough to be mounted on the finger, and (2) evaluations ofstate-of-the-art recognition algorithms applied to our dataset - achieving anaccuracy >95%. Throughout the paper, we will discuss previous work, evaluatethe current work, and finally, suggest the project's future direction.

HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color and Texture with a Finger-Mounted Camera