Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

A system for large vocabulary sign search

Abstract: A method is presented to help users look up the meaning of an unknown sign from American Sign Language (ASL) The user submits a video of the unknown sign as a query, and the system retrieves the most similar signs from a database of sign videos The user then reviews the retrieved videos to identify the video displaying the sign of interest Hands are detected in a semi-automatic way: the system performs some hand detection and tracking, and the user has the option to verify and correct the detected hand locations Features are extracted based on hand motion and hand appearance Similarity between signs is measured by combining dynamic time warping (DTW) scores, which are based on hand motion, with a simple similarity measure based on hand appearance In user-independent experiments, with a system vocabulary of 1,113 signs, the correct sign was included in the top 10 matches for 78% of the test queries

GaitLock: Protect Virtual and Augmented Reality Headsets Using Gait

Abstract: With the fast penetration of commercial Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security issues of those devices have attracted significant interests from both academia and industry. Modern VR/AR systems typically use head-mounted devices (i.e., headsets) to interact with users, and often store private user data, e.g., social network accounts, online transactions or even payment information. This poses significant security threats, since in practice the headset can be potentially obtained and accessed by unauthenticated parties, e.g., identity thieves, and thus cause catastrophic breach. In this paper, we propose a novel GaitLock system, which can reliably authenticate users using their gait signatures. Our system doesn't require extra hardware, e.g., fingerprint sensors or retina scanners, but only uses the on-board inertial measurement units (IMUs) equipped in almost all mainstream VR/AR headsets to authenticate the legitimate users from intruders, by simply asking them to walk a few steps. To achieve that, we propose a new gait recognition model Dynamic-SRC, which combines the strength of Dynamic Time Warping (DTW) and Sparse Representation Classifier (SRC), to extract unique gait patterns from the inertial signals during walking. We implement GaitLock on Google Glass (a typical AR headset), and extensive experiments show that GaitLock outperforms the state-of-the-art systems significantly in recognition accuracy ($>$>98 percent success in 5 steps), and is able to run in-situ on the resource-constrained VR/AR headsets without incurring high energy cost.

Face recognition from face profile using dynamic time warping

Abstract: Most of the current profile recognition algorithms depend on the correct detection of fiducial points and the determination of relationships among these fiducial points. Unfortunately, some features such as concave nose, protruding lips, flat chin, etc., make detection of such points difficult and unreliable. Also, the number and position of fiducial points vary when expression changes even for the same person. In this paper, a curvature-based matching approach is presented, which does not require the extraction of all the fiducial points, but uses information contained in the profile. The scale space filtering is used to smooth the profile and then the curvature of the filtered profile is computed. Using the curvature value, the fiducial points, such as nasion and throat can be reliably extracted using a fast and simple method. Then a dynamic time warping method is applied to match the face profile portion from nasion to throat based on the curvature value. Experiments are performed on two profile face image databases. Recognition rates and conclusion are presented and discussed.

Jackknife: A Reliable Recognizer with Few Samples and Many Modalities

Abstract: Despite decades of research, there is yet no general rapid prototyping recognizer for dynamic gestures that can be trained with few samples, work with continuous data, and achieve high accuracy that is also modality-agnostic. To begin to solve this problem, we describe a small suite of accessible techniques that we collectively refer to as the Jackknife gesture recognizer. Our dynamic time warping based approach for both segmented and continuous data is designed to be a robust, go-to method for gesture recognition across a variety of modalities using only limited training samples. We evaluate pen and touch, Wii Remote, Kinect, Leap Motion, and sound-sensed gesture datasets as well as conduct tests with continuous data. Across all scenarios we show that our approach is able to achieve high accuracy, suggesting that Jackknife is a capable recognizer and good first choice for many endeavors.