text: We introduce Pixie, a novel, camera based two factor authentication solution for mobile and wearable devices. A quick and familiar user action of snapping a photo is sufficient for Pixie to simultaneously perform a graphical password authentication and a physical token based authentication, yet it does not require any expensive, uncommon hardware. Pixie establishes trust based on both the knowledge and possession of an arbitrary physical object readily accessible to the user, called trinket. Users choose their trinkets similar to setting a password, and authenticate by presenting the same trinket to the camera. The fact that the object is the trinket, is secret to the user. Pixie extracts robust, novel features from trinket images, and leverages a supervised learning classifier to effectively address inconsistencies between images of the same trinket captured in different circumstances. Pixie achieved a false accept rate below 0.09 in a brute force attack with 14.3 million authentication attempts, generated with 40,000 trinket images that we captured and collected from public datasets. We identify master images, that match multiple trinkets, and study techniques to reduce their impact. In a user study with 42 participants over 8 days in 3 sessions we found that Pixie outperforms text based passwords on memorability, speed, and user preference. Furthermore, Pixie was easily discoverable by new users and accurate under field use. Users were able to remember their trinkets 2 and 7 days after registering them, without any practice between the 3 test dates.
  @cite_73: Wearable devices are computationally rich, portable and wearable to suit users in various needs and situations. Still, users of wearable devices suffer from lack of efficient input methods and proper feedback modality. In this paper, we design and implement a novel input method exploiting readily available ambient light sensor on wearable devices. We present concepts and implementation results of re-purposing the light sensor for user input and processing UI events. Furthermore, a PIN entry application based on the proposed input method is implemented to demonstrate a device authentication scenario for wearables.
  @cite_13: In this paper we present zSense, which provides greater input expressivity for spatially limited devices such as smart wearables through a shallow depth gesture recognition system using non-focused infrared sensors. To achieve this, we introduce a novel Non-linear Spatial Sampling (NSS) technique that significantly cuts down the number of required infrared sensors and emitters. These can be arranged in many different configurations; for example, number of sensor emitter units can be as minimal as one sensor and two emitters. We implemented different configurations of zSense on smart wearables such as smartwatches, smartglasses and smart rings. These configurations naturally fit into the flat or curved surfaces of such devices, providing a wide scope of zSense enabled application scenarios. Our evaluations reported over 94.8 gesture recognition accuracy across all configurations.
  @cite_6: We propose SkinWatch, a new interaction modality for wearable devices. SkinWatch provides gesture input by sensing deformation of the skin under a wearable wrist device, also known as a smart watch. A gesture command that is matched by learning data and two-dimensional linear input recognizes the gestures. The sensing part is small, thin, and stable, to accept accurate input via a user's skin. We also implement an anti-error mechanism to prevent unexpected input when the user moves or rotates his or her forearm. The whole sensor costs less than $1.50 and the sensor layer does not exceed a height of more than 3 mm in this prototype. We demonstrate sample applications with a practical task; using two-finger skin gesture input.
summary: To address the limited input space of wearable devices, available sensors (e.g. camera) are commonly exploited to provide alternative input techniques: Omata and Imai @cite_6 identify the input gesture of the user by sensing the deformation of the skin under the smartwatch. @cite_13 use infrared sensors to capture the gesture input of the user to interact with a wearable device. @cite_73 exploit the ambient light sensor to capture the changes in light state as a form of PIN entry for wearable devices.